[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"portal-settings:figure:fr":3,"public-menus:all":45,"post:what-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter:fr":531,"related:post:what-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter:fr:1":1705},{"statusCode":4,"data":5,"message":44},200,{"tenantId":6,"lang":7,"defaultLang":8,"siteUrl":9,"contactEmail":10,"brandName":11,"logoUrl":12,"siteName":11,"siteDescription":13,"ogImage":14,"robotsIndex":15,"socialLinks":14,"reservedSlugs":14,"seoPolicy":16},"figure","fr","en","https:\u002F\u002Ffigure.rocks","info@stajic.de","Figure Rocks","\u002Ffavicon-32x32.png","",null,true,{"branding":17,"relatedContent":18,"crossDomainLinks":19},{"logoUrl":12},{"enabled":15},[20,23,26,29,32,35,38,41],{"url":21,"label":22,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Floving.rocks","loving.rocks",{"url":24,"label":25,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fstajic.de","stajic.de",{"url":27,"label":28,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fbazify.com","bazify.com",{"url":30,"label":31,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fbazify.de","bazify.de",{"url":33,"label":34,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002F2mesta.com","2mesta.com",{"url":36,"label":37,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002F2mesta.de","2mesta.de",{"url":39,"label":40,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fbazify.at","bazify.at",{"url":42,"label":43,"isActive":15,"showInFooter":15,"includeInSameAs":15},"https:\u002F\u002Fweb-hoch3.de","web-hoch3.de","Portal settings resolved",[46],{"id":47,"name":48,"location":49,"isActive":15,"isDefault":15,"items":50},2,"Main Menu","sidebar",[51,65,189,282,358,424],{"id":52,"title":53,"url":61,"target":62,"icon":63,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":64},"1",{"de":54,"en":55,"es":56,"fr":57,"it":55,"ru":58,"sr":59,"zh":60},"Startseite","Home","Inicio","Accueil","Главная","Početna","首页","\u002F","_self","i-lucide-home",[],{"id":66,"title":67,"url":76,"target":62,"icon":77,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":78},"3",{"de":68,"en":69,"es":70,"fr":71,"it":72,"ru":73,"sr":74,"zh":75},"Spiele","Games","Juegos","Jeux","Giochi","Игры","Igre","游戏","\u002Fgames","i-lucide-gamepad-2",[79,92,107,121,135,148,162,176],{"id":80,"title":81,"url":76,"target":62,"icon":90,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":91},"3-1",{"de":82,"en":83,"es":84,"fr":85,"it":86,"ru":87,"sr":88,"zh":89},"Spiele-Hub","Games Hub","Centro de juegos","Hub de jeux","Hub Giochi","Игровой центр","Centar za igre","游戏中心","i-lucide-layout-grid",[],{"id":93,"title":94,"url":103,"target":62,"icon":104,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":106},"1-1",{"de":95,"en":96,"es":97,"fr":98,"it":99,"ru":100,"sr":101,"zh":102},"Neu & Angesagt","New & Trending","Novedades & Tendencias","Nouveautés & Tendances","Novità & Tendenze","Новинки & тренды","Novo & Popularno","新品 & 热门","\u002Fnews\u002Fgaming-news","i-lucide-sparkles","custom",[],{"id":108,"title":109,"url":118,"target":62,"icon":119,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":120},"1-2",{"de":110,"en":111,"es":112,"fr":113,"it":114,"ru":115,"sr":116,"zh":117},"Angebote","Deals","Ofertas","Offres","Offerte","Акции","Ponude","优惠","\u002Fdeals","i-lucide-badge-percent",[],{"id":122,"title":123,"url":132,"target":62,"icon":133,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":134},"1-3",{"de":124,"en":125,"es":126,"fr":127,"it":128,"ru":129,"sr":130,"zh":131},"Release-Kalender","Release Calendar","Calendario de lanzamientos","Calendrier des sorties","Calendario delle uscite","Календарь релизов","Kalendar izdanja","发布日历","\u002Freleases","i-lucide-calendar-days",[],{"id":136,"title":137,"url":145,"target":62,"icon":146,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":147},"3-2",{"de":138,"en":139,"es":140,"fr":141,"it":142,"sr":143,"zh":144},"amiibo-kompatible Spiele","amiibo-Compatible Games","Juegos compatibles con Amiibo","Jeux compatibles Amiibo","Giochi compatibili con amiibo","Amiibo kompatibilne igre","Amiibo 兼容游戏","\u002Freference\u002Fcompatibility","i-lucide-check-circle-2",[],{"id":149,"title":150,"url":159,"target":62,"icon":160,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":161},"3-3",{"de":151,"en":152,"es":153,"fr":154,"it":155,"ru":156,"sr":157,"zh":158},"Wie amiibo funktionieren","How amiibo work","Cómo funcionan los Amiibo","Comment fonctionnent les Amiibo","Come funzionano gli Amiibo","Как работают Amiibo","Kako Amiibo rade","Amiibo 如何运作","\u002Fgames\u002Fhow-amiibo-unlocks-work","i-lucide-puzzle",[],{"id":163,"title":164,"url":173,"target":62,"icon":174,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":175},"3-4",{"de":165,"en":166,"es":167,"fr":168,"it":169,"ru":170,"sr":171,"zh":172},"Beste Amiibo pro Spiel","Best amiibo per Game","Mejores Amiibo por juego","Meilleurs Amiibo par jeu","Migliori Amiibo per gioco","Лучшие Amiibo по играм","Najbolji Amiibo po igri","各游戏最佳 Amiibo","\u002Fgames\u002Fbest-amiibo-per-game","i-lucide-award",[],{"id":177,"title":178,"url":186,"target":62,"icon":187,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":188},"3-5",{"de":179,"en":180,"es":181,"fr":182,"it":183,"ru":184,"sr":185},"Freischaltbares & Belohnungen","Unlockables & Rewards","Desbloqueables & Recompensas","Déblocables & Récompenses","Sbloccabili & Ricompense","Разблокировки & награды","Otključavanja & nagrade","\u002Fgames\u002Funlocks-and-benefits","i-lucide-gift",[],{"id":190,"title":191,"url":199,"target":62,"icon":200,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":201},"4",{"de":192,"en":193,"es":194,"it":195,"ru":196,"sr":197,"zh":198},"Sammeln","Collecting","Coleccionismo","Collezionismo","Коллекционирование","Kolekcionarstvo","收藏","\u002Fcollecting","i-lucide-gem",[202,213,227,240,254,268],{"id":203,"title":204,"url":211,"target":62,"icon":90,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":212},"4-1",{"de":205,"en":206,"fr":207,"it":208,"ru":209,"zh":210},"Sammel-Hub","Collecting Hub","Hub de collecte","Hub di raccolta","Центр сбора","收藏中心","\u002Famiibo\u002Fcollecting",[],{"id":214,"title":215,"url":224,"target":62,"icon":225,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":226},"4-2",{"de":216,"en":217,"es":218,"fr":219,"it":220,"ru":221,"sr":222,"zh":223},"Kaufberatung","Buying Guide","Guía de compra","Guide d'achat","Guida all'acquisto","Гид покупателя","Vodič za kupovinu","购买指南","\u002Famiibo-library\u002Famiibo-buying-smart","i-lucide-shopping-bag",[],{"id":228,"title":229,"url":237,"target":62,"icon":238,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":239},"4-3",{"de":230,"en":230,"es":231,"fr":232,"it":233,"ru":234,"sr":235,"zh":236},"Fake vs Real","Falso vs. Real","Faux vs Vrai","Falso vs Vero","Фейк vs Оригинал","Lažno vs Pravo","真假对比","\u002Famiibo-library\u002Famiibo-buying-smart\u002Favoid-fake-listings","i-lucide-scan",[],{"id":241,"title":242,"url":251,"target":62,"icon":252,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":253},"4-4",{"de":243,"en":244,"es":245,"fr":246,"it":247,"ru":248,"sr":249,"zh":250},"Aufbewahrung & Präsentation","Storage & Display","Almacenamiento & exhibición","Rangement & Présentation","Contenitori & Esposizione","Хранение & витрины","Odlaganje & izlaganje","存储 & 展示","\u002Famiibo-library\u002Famiibo-care-and-storage","i-lucide-box",[],{"id":255,"title":256,"url":265,"target":62,"icon":266,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":267},"4-5",{"de":257,"en":258,"es":259,"fr":260,"it":261,"ru":262,"sr":263,"zh":264},"Preisübersicht","Price Guide","Guía de precios","Guide des prix","Guida ai prezzi","Гид по ценам","Cenovnik","价格指南","\u002Fcollections\u002Fprice-guide","i-lucide-bar-chart-3",[],{"id":269,"title":270,"url":279,"target":62,"icon":280,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":281},"4-6",{"de":271,"en":272,"es":273,"fr":274,"it":275,"ru":276,"sr":277,"zh":278},"Raritäten","Rare Finds","Hallazgos únicos","Trouvailles rares","Pezzi rari","Редкие находки","Retki nalazi","稀有发现","\u002Fcollections\u002Frare-and-notable","i-lucide-trophy",[],{"id":283,"title":284,"url":292,"target":62,"icon":293,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":294},"5",{"de":285,"en":286,"es":287,"fr":286,"it":288,"ru":289,"sr":290,"zh":291},"Technik","Tech","Tecnología","Tecnologia","Технологии","Tehnologija","科技","\u002Ftech","i-lucide-cpu",[295,306,318,331,344],{"id":296,"title":297,"url":292,"target":62,"icon":90,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":305},"5-1",{"de":298,"en":298,"es":299,"fr":300,"it":301,"ru":302,"sr":303,"zh":304},"Tech Hub","Centro tecnológico","Hub Tech","Hub Tecnologico","Технохаб","Tehnološki centar","科技中心",[],{"id":307,"title":308,"url":315,"target":62,"icon":316,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":317},"5-2",{"de":309,"en":310,"fr":311,"it":312,"ru":313,"zh":314},"Audio- & Mikrofonqualität","Audio & Mic Quality","Qualité audio & micro","Qualità audio & mic","Качество звука & микрофона","音频 & 麦克风质量","\u002Fgear\u002Faudio","i-lucide-mic",[],{"id":319,"title":320,"url":328,"target":62,"icon":329,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":330},"5-3",{"de":321,"en":322,"es":323,"fr":324,"it":325,"sr":326,"zh":327},"Controller & Zubehör","Controllers & Accessories","Mandos & accesorios","Manettes & accessoires","Controller & Accessori","Kontroleri & dodatna oprema","控制器 & 配件","\u002Fgear\u002Fcontrols","i-lucide-joystick",[],{"id":332,"title":333,"url":341,"target":62,"icon":342,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":343},"5-4",{"de":334,"en":335,"es":336,"fr":337,"it":338,"ru":339,"sr":340},"Displays & Aufnahme","Displays & Capture","Pantallas & Captura","Écrans & Capture","Display & Acquisizione","Дисплеи & Захват","Ekrani & snimanje","\u002Fgear\u002Fdisplays","i-lucide-monitor",[],{"id":345,"title":346,"url":355,"target":62,"icon":356,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":357},"5-5",{"de":347,"en":348,"es":349,"fr":350,"it":351,"ru":352,"sr":353,"zh":354},"Netzwerkstabilität","Network Stability","Estabilidad de la red","Stabilité du réseau","Stabilità della rete","Стабильность сети","Stabilnost mreže","网络稳定性","\u002Fplaybooks\u002Fnetwork-stability","i-lucide-wifi",[],{"id":359,"title":360,"url":368,"target":62,"icon":369,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":370},"6",{"de":361,"en":361,"es":362,"fr":363,"it":364,"ru":365,"sr":366,"zh":367},"Shop","Tienda","Boutique","Negozio","Магазин","Prodavnica","商店","\u002Fshop","i-lucide-shopping-cart",[371,383,391,399,412],{"id":372,"title":373,"url":368,"target":62,"icon":381,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":382},"6-1",{"de":374,"en":375,"es":376,"fr":377,"ru":378,"sr":379,"zh":380},"Shop-Hub","Shop Hub","Centro de compras","Espace Boutique","Центр магазина","Centar za kupovinu","购物中心","i-lucide-store",[],{"id":384,"title":385,"url":388,"target":62,"icon":389,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":390},"6-2",{"de":386,"en":386,"es":387,"fr":387,"it":387,"sr":387,"zh":387},"amiibo","Amiibo","\u002Famiibo-shop","i-lucide-scan-line",[],{"id":392,"title":393,"url":396,"target":62,"icon":397,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":398},"6-3",{"de":394,"en":394,"es":394,"fr":394,"it":394,"ru":394,"sr":394,"zh":395},"LEGO","乐高","\u002Flego-shop","i-lucide-blocks",[],{"id":400,"title":401,"url":409,"target":62,"icon":410,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":411},"6-4",{"de":402,"en":403,"es":404,"fr":405,"it":406,"sr":407,"zh":408},"Figuren & Sammlerstücke","Figures & Collectibles","Figuras & Coleccionables","Figurines & Objets de collection","Figure & Collezionismo","Figure & kolekcionarstvo","手办 & 收藏品","\u002Fshop\u002Ffigures","i-lucide-package",[],{"id":413,"title":414,"url":421,"target":62,"icon":422,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":423},"6-5",{"de":415,"en":415,"fr":416,"it":417,"ru":418,"sr":419,"zh":420},"Gaming Gear","Équipement gaming","Accessori gaming","Игровое снаряжение","Gejming oprema","游戏装备","\u002Fgaming-gear-shop","i-lucide-headphones",[],{"id":425,"title":426,"url":427,"target":62,"icon":389,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":428},"2",{"de":386,"en":386,"es":386,"fr":386,"it":386,"ru":386,"sr":386,"zh":386},"\u002Famiibo",[429,440,447,461,475,489,503,517],{"id":430,"title":431,"url":427,"target":62,"icon":90,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":439},"2-1",{"de":432,"en":433,"es":434,"fr":435,"it":435,"ru":436,"sr":437,"zh":438},"amiibo-Hub","amiibo Hub","Centro amiibo","Hub amiibo","Хаб amiibo","amiibo centar","amiibo 中心",[],{"id":441,"title":442,"url":444,"target":62,"icon":445,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":446},"1772724791061",{"de":443,"en":443},"Franchise","\u002Famiibo\u002Ffranchise","i-lucide-star",[],{"id":448,"title":449,"url":458,"target":62,"icon":459,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":460},"2-3",{"de":450,"en":451,"es":452,"fr":453,"it":454,"ru":455,"sr":456,"zh":457},"Region & Verpackung (EU\u002FUS\u002FJP)","Region & Packaging (EU\u002FUS\u002FJP)","Región y embalaje (UE\u002FEE. UU.\u002FJP)","Région & Emballage (UE\u002FUS\u002FJP)","Regione & Confezione (EU\u002FUS\u002FJP)","Регион & упаковка (EU\u002FUS\u002FJP)","Region & pakovanje (EU\u002FUS\u002FJP)","地区 & 包装 (EU\u002FUS\u002FJP)","\u002Famiibo-library\u002Famiibo-editions-and-regions","i-lucide-globe",[],{"id":462,"title":463,"url":472,"target":62,"icon":473,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":474},"2-4",{"de":464,"en":465,"es":466,"fr":467,"it":468,"ru":469,"sr":470,"zh":471},"Schnellidentifikations-Checkliste","Fast Identification Checklist","Lista de identificación rápida","Liste d'identification rapide","Checklist identificazione rapida","Чек-лист быстрой идентификации","Kontrolna lista za brzu identifikaciju","快速识别清单","\u002Famiibo\u002Fidentify-checklist","i-lucide-list-checks",[],{"id":476,"title":477,"url":486,"target":62,"icon":487,"isActive":15,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":488},"2-6",{"de":478,"en":479,"es":480,"fr":481,"it":482,"ru":483,"sr":484,"zh":485},"Tipps für Sealed-Sammlungen","Sealed Collection Tips","Consejos de colección sellada","Conseils collection scellée","Consigli collezione sigillata","Советы по коллекции Sealed","Saveti za zapečaćene kolekcije","密封收藏贴士","\u002Famiibo\u002Fsealed-tips","i-lucide-shield-check",[],{"id":490,"title":491,"url":500,"target":62,"icon":501,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":502},"2-5",{"de":492,"en":493,"es":494,"fr":495,"it":496,"ru":497,"sr":498,"zh":499},"Zustand & Bewertung","Condition & Grading","Estado & Graduación","État & Évaluation","Condizioni & Valutazione","Состояние & Оценка","Stanje & ocenjivanje","品相与分级","\u002Famiibo-library\u002Famiibo-condition-and-grading","i-lucide-badge-check",[],{"id":504,"title":505,"url":514,"target":62,"icon":515,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":516},"2-2",{"de":506,"en":507,"es":508,"fr":509,"it":510,"ru":511,"sr":512,"zh":513},"Ausgaben & Nachdrucke","Editions & Reprints","Ediciones & Reimpresiones","Éditions & Réimpressions","Edizioni & ristampe","Издания & переиздания","Izdanja & reizdanja","版本 & 重印","\u002Famiibo\u002Fcollecting\u002F","i-lucide-layers",[],{"id":518,"title":519,"url":528,"target":62,"icon":529,"isActive":15,"type":105,"productId":14,"categoryId":14,"shopCategoryId":14,"articleId":14,"pageId":14,"portfolioId":14,"children":530},"2-7",{"de":520,"en":521,"es":522,"fr":523,"it":524,"ru":525,"sr":526,"zh":527},"Nachdruck-Timeline","Reprint Timeline","Cronología de reimpresión","Chronologie des réimpressions","Cronologia ristampe","Хронология переизданий","Hronologija reprinta","重印时间轴","\u002Freference\u002Ftimelines\u002Famiibo-reprint-timeline","i-lucide-timer",[],{"statusCode":4,"data":532,"message":1704},{"id":533,"title":534,"slug":535,"content":536,"contentJson":537,"excerpt":1033,"featuredImage":1034,"featuredImageAlt":1035,"featuredImageCaption":14,"featuredImageTitle":14,"featuredImageCopyright":14,"featuredImageAuthor":14,"featuredImageSourceUrl":14,"featuredImageLicense":14,"featuredImageIsAiGenerated":705,"status":1036,"publishedAt":1037,"createdAt":1038,"updatedAt":1039,"seoLocalePaths":1040,"categories":1049,"author":1077,"translations":1080},"455","Et si un jeu pouvait prédire son prochain accès mémoire ? Le préchargement par IA et l'avenir des saccades","what-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u003Cp>Un jeu connaît rarement l'avenir exact, mais il en sait bien plus sur les prochaines images qu'une application généraliste. Il connaît le vecteur de caméra, la vélocité du joueur, les cellules visibles, les portails à proximité, les dépendances des ressources, l'état de l'animation et souvent quels shaders ou effets sont susceptibles de s'exécuter ensuite. Cela fait du jeu vidéo une cible particulièrement intéressante pour la gestion prédictive de la mémoire.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Réponse directe\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>Le préchargement assisté par IA pourrait plausiblement réduire certains blocages liés à la mémoire, mais ce ne serait pas un bouton anti-saccade universel.\u003C\u002Fstrong> La recherche montre déjà que les préchargeurs à apprentissage profond améliorent la prédiction des pages en mémoire unifiée CPU-GPU. Un moteur de jeu pourrait ajouter ce que ces benchmarks n'ont pas : un état explicite du monde et du joueur. L'hypothèse Figure Rocks est de combiner les deux.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Hypothèse Figure Rocks, pas une fonctionnalité annoncée\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Le terme \u003Cstrong>Predictive Gaming Memory\u003C\u002Fstrong> dans cet article décrit une architecture proposée : connaissance déterministe du moteur + prédiction d'accès apprise + préchargement + politique de résidence. Ce n'est pas un nom de produit NVIDIA, Microsoft, Epic ou AMD.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sommaire\">\u003Cstrong class=\"editorjs-toc__title\">Sommaire\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">Séparer d'abord les blocages mémoire des autres saccades\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-8\" class=\"editorjs-toc__link\">Le préchargement existe déjà ; la nouvelle question est comment il prédit\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-11\" class=\"editorjs-toc__link\">Il existe déjà des preuves de recherche pour le préchargement GPU appris\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-16\" class=\"editorjs-toc__link\">Les jeux disposent d'informations supplémentaires qu'un préchargeur générique n'a pas\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-19\" class=\"editorjs-toc__link\">Le moteur peut combiner le futur connu avec le futur prédit\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">Un exemple simple : courir vers une porte\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-23\" class=\"editorjs-toc__link\">Pourquoi cela pourrait compter davantage à mesure que le rendu neuronal se développe\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-26\" class=\"editorjs-toc__link\">Les modes de défaillance sont aussi importants que l'opportunité\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-29\" class=\"editorjs-toc__link\">Comment vous le testeriez dans un vrai moteur\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-31\" class=\"editorjs-toc__link\">Où cette hypothèse s'arrête\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">Un lien léger avec l'architecture générale de l'IA\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">Source principale\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Séparer d'abord les blocages mémoire des autres saccades\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Sources de saccades qui peuvent se ressembler\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Chemin mémoire\u002Fdonnées\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Autre chemin\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Ressource absente de la mémoire rapide\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Possible\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Le stockage ou la décompression peuvent aussi être impliqués\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Shader non prêt\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Peut impliquer des caches\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">La compilation\u002Fcréation de pipeline peut être la vraie cause\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Pic de simulation CPU\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Le comportement du cache\u002Fmémoire peut contribuer\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">La logique de jeu ou le travail de draw-call peut dominer\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">À-coup en ligne\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Généralement pas la cause principale\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Le serveur ou le timing des paquets peut dominer\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Pression d'éviction\u002Frésidence\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Directement pertinent\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Peut être mal diagnostiquée à partir des compteurs d'allocation seuls\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">Pourquoi les jeux PC saccadent lors de la compilation des shaders — et comment la livraison avancée de shaders change la donne\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Les saccades de shaders sont un problème différent de la prédiction des pages mémoire et ne doivent pas être regroupées dans un seul diagnostic.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Séparer les saccades de shaders des blocages mémoire →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-8\">Le préchargement existe déjà ; la nouvelle question est comment il prédit\u003C\u002Fh2>\n\u003Cp>Le préchargement traditionnel tente de déplacer les données avant qu'un processeur ne les demande explicitement. Les préchargeurs matériels exploitent les motifs d'accès. Les moteurs diffusent les cellules du monde avant que le joueur ne les atteigne. Les systèmes d'exploitation et les runtimes gardent chaudes les pages probablement nécessaires. La partie difficile est la fausse prédiction : précharger trop peu et vous bloquez encore ; précharger trop et vous gaspillez de la bande passante et évincez des données utiles.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Le compromis de base du préchargement\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Observer\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Collecter les accès récents, l'état du monde et le contexte de charge de travail.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Prédire\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Estimer quelles pages\u002Fressources\u002Fshaders seront nécessaires ensuite.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Déplacer\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Transférer ou rendre résidentes ces ressources avant la demande.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Utiliser\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le GPU consomme la ressource sans attendre si la prédiction était correcte.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Corriger\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Évincer les mauvaises prédictions et mettre à jour le modèle\u002Fla politique.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-11\">Il existe déjà des preuves de recherche pour le préchargement GPU appris\u003C\u002Fh2>\n\u003Cp>Une étude publiée sur le préchargement basé sur l'apprentissage profond pour la mémoire virtuelle unifiée CPU-GPU a utilisé un Transformer pour modéliser le contexte global d'accès mémoire, puis en a dérivé un prédicteur plus léger. Sur 11 benchmarks intensifs en mémoire, l'article a rapporté une amélioration de performance de 10,89 % par rapport au framework comparé, un taux de succès des pages en mémoire de périphérique de 89,02 % contre 76,10 %, et 11,05 % de trafic d'interconnexion CPU-GPU en moins.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Résultat de recherche\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Valeur rapportée\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Ce que cela soutient\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Ce que cela ne prouve PAS\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Performance UVM apprise\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">+10,89 %\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">La prédiction d'accès apprise peut battre une ligne de base de préchargement UVM antérieure\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Un jeu commercial gagne 10,89 % de FPS\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Taux de succès des pages UVM\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">89,02 % vs 76,10 %\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">La prédiction peut modifier matériellement l'endroit où les données demandées sont trouvées\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Chaque texture\u002Fpage de jeu est également prévisible\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Trafic d'interconnexion CPU–GPU\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">−11,05 %\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Un meilleur placement peut réduire les mouvements réactifs\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le trafic PCIe diminue toujours dans les jeux\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Efficacité du noyau d'attention AAAI\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">2,15×\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le préchargement peut masquer un accès mémoire coûteux derrière un calcul utile\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Les noyaux de jeu reçoivent 2,15×\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Débit de bout en bout AAAI\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Jusqu'à 1,97×\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le masquage de latence peut se propager au-delà d'un noyau\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Un multiplicateur de fréquence d'images de jeu de 1,97×\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Ne copiez pas ces chiffres sur les jeux\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Ces résultats proviennent de l'ensemble de benchmarks de l'article, \u003Cstrong>pas de moteurs de jeu commerciaux\u003C\u002Fstrong>. Ils prouvent que la prédiction d'accès mémoire apprise peut fonctionner dans des charges de travail UVM GPU. Ils ne prouvent pas un gain de 10,89 % de FPS ni une réduction spécifique des saccades dans un jeu.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cp>L'étude sur l'UVM apprise est utile car elle prédit quel accès de page est probable à partir du comportement mémoire. Le travail AAAI 2026 sur le cache KV est utile pour une autre raison : il montre qu'un ordonnanceur peut exploiter des fenêtres de bande passante autrement inactives pour décider quand déplacer des données vers un cache plus rapide avant la demande. Ensemble, ils soutiennent deux mécanismes distincts dans l'hypothèse Figure Rocks — la prédiction et le timing — bien qu'aucun des deux articles ne soit un benchmark de jeu.\u003C\u002Fp>\n\u003Ch2 id=\"section-16\">Les jeux disposent d'informations supplémentaires qu'un préchargeur générique n'a pas\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">La boucle Predictive Gaming Memory\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Lire l'état déterministe\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Direction de caméra, position, vélocité, graphe de niveau, visibilité, animation, état de quête ou de rencontre.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Lire l'historique d'accès\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quelles pages de texture, maillages, shaders, clusters de géométrie et données de modèle ont été récemment utilisés.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Prédire l'ensemble de travail à court terme\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Estimer les ressources probablement nécessaires plusieurs images ou secondes à l'avance selon le coût.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Attribuer confiance et coût\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Une ressource de 4 Mo à 95 % de probabilité est une décision différente d'une région de 2 Go à 20 % de probabilité.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Précharger dans un budget de bande passante\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Ne pas laisser la prédiction elle-même saturer le chemin nécessaire au rendu actuel.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">6. Mesurer les échecs et la pollution\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Suivre les blocages évités, les mauvaises prédictions et les données utiles évincées.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">7. S'adapter\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Mettre à jour le prédicteur ou la politique du moteur pour la scène et le comportement réels du joueur.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">L'hybride intéressant\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Un jeu n'a pas à choisir entre streaming écrit à la main et IA. \u003Cstrong>La conception la plus solide est peut-être hybride :\u003C\u002Fstrong> des règles déterministes garantissent les ressources critiques, tandis qu'un prédicteur appris classe le travail optionnel ou à court terme, choisit la profondeur de préchargement et ne dépense que la marge de bande passante sûre. Si la confiance est faible, le système revient aux règles écrites.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-19\">Le moteur peut combiner le futur connu avec le futur prédit\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Connaissance déterministe vs prédiction apprise\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Le moteur sait déjà\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Le prédicteur peut estimer\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">État de la caméra \u002F du portail\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Vue actuelle, frustum, graphe de salles, portail visible\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Dans quelle direction le joueur est susceptible de tourner ensuite\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Mouvement du joueur\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Vélocité, entrées, contraintes de navigation\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Chemin de traversée probable plusieurs instants à l'avance\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Dépendances des ressources\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Dépendances pour une cellule de niveau ou un objet connu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Quelles ressources voisines optionnelles deviendront sollicitées\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">État du LOD\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Distance et seuils actuels\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Quelles transitions de LOD sont probables bientôt\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Graphe de matériaux\u002Fshader\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Shaders requis par le contenu visible connu\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Quelles variantes sont susceptibles d'être demandées ensuite\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Force du cœur\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Connaît les règles strictes du niveau, les dépendances et les transitions garanties\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Classe l'accès futur ambigu à partir du comportement et du contexte\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Mode de défaillance\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Peut être conservateur et charger de larges ensembles\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Peut se tromper et polluer le cache ou la mémoire\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Meilleur usage\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Ressources critiques et transitions garanties\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Priorisation optionnelle dans un budget de bande passante borné\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Politique de sécurité\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Facile à raisonner et à tester\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">Nécessite des seuils de confiance, de la télémétrie et un repli déterministe\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-21\">Un exemple simple : courir vers une porte\u003C\u002Fh2>\n\u003Cp>Imaginez un joueur sprintant dans un couloir vers une porte qui s'ouvre sur une place de marché dense. Un système de streaming conventionnel connaît déjà la zone suivante et peut commencer à la charger. La mémoire prédictive ajoute une seconde couche : en se basant sur la direction de la caméra, le mouvement, les choix antérieurs du joueur et l'état actuel de la mission, elle peut classer quel sous-ensemble de textures, de données d'animation de PNJ, de shaders et de géométrie est le plus susceptible d'être nécessaire en premier.\u003C\u002Fp>\n\u003Ch2 id=\"section-23\">Pourquoi cela pourrait compter davantage à mesure que le rendu neuronal se développe\u003C\u002Fh2>\n\u003Cp>Les graphismes neuronaux ajoutent de nouvelles classes de données : poids de modèles, représentations latentes ou compressées, tampons d'historique et tenseurs intermédiaires. Si ces ressources entrent en concurrence avec les textures et la géométrie, la résidence devient plus complexe. La planification prédictive pourrait décider non seulement quelle ressource déplacer, mais aussi quelle représentation conserver : données brutes, représentation neuronale compressée ou forme reconstructible.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">DLSS 5 n'est pas qu'un upscaling : ce que le rendu neuronal génératif change réellement\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Le rendu neuronal change les données dont une image a besoin et le moment où elle en a besoin.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire l'analyse sur le rendu neuronal →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-26\">Les modes de défaillance sont aussi importants que l'opportunité\u003C\u002Fh2>\n\u003Cp>Le prefetch n'est pas gratuit. Une mauvaise prédiction peut récupérer des ressources que le joueur ne verra jamais, évincer des pages utiles et entrer en concurrence avec le trafic critique pour l'image. L'objectif correct n'est donc pas le prefetch maximal ; c'est le taux de réussite utile maximal dans un budget de bande passante et de résidence borné.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Mode de défaillance\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Ce qui se passe\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Idée d'atténuation\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Mauvaise prédiction\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Des données erronées sont préchargées\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Seuils de confiance ; réserver du budget pour la demande déterministe\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pollution du cache\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Des données actuelles utiles sont évincées\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Séparer les classes de résidence prédites et protégées\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Contention de bande passante\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le prefetch vole de la bande passante à l'image actuelle\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Planifier uniquement dans les fenêtres de marge ; imposer un budget par image\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Changement de comportement\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le joueur fait demi-tour ou se téléporte\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Annulation peu coûteuse et repli rapide\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Surcoût du modèle\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le prédicteur coûte plus qu'il n'économise\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Petits modèles, entraînement hors ligne ou prédicteurs légers dérivés de règles\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Saccade non liée à la mémoire\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Le problème persiste\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Garder le diagnostic séparé : les chemins shader, CPU, réseau et stockage nécessitent leurs propres tests\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-29\">Comment vous le testeriez dans un vrai moteur\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Évaluation de la mémoire prédictive\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">A. Établir la référence\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Capturer les pics de temps d'image, les défauts de page\u002Fmigrations, les échecs de résidence et le volume de transfert de données sans prédiction.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">B. Ajouter uniquement le prefetch déterministe\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Mesurer ce que la connaissance conventionnelle du moteur résout déjà.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">C. Ajouter le classement appris\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Utiliser le même budget de mémoire et de bande passante pour que la comparaison soit équitable.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">D. Mesurer le taux de réussite et la pollution\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Suivre les octets préchargés utiles par rapport aux octets gaspillés et aux évictions.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">E. Mesurer la distribution des temps d'image\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le FPS moyen ne suffit pas ; inspecter la latence de queue et la fréquence des saccades.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">F. Tester les changements de comportement\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Virages rapides, téléportation, imprévisibilité multijoueur et scènes procédurales.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">G. Comparer le coût\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le temps CPU\u002FGPU du prédicteur + le surcoût mémoire doivent être inférieurs aux blocages qu'il élimine.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-31\">Où cette hypothèse s'arrête\u003C\u002Fh2>\n\u003Cp>La mémoire prédictive ne peut pas rendre infiniment grand un pool de mémoire trop petit. Elle ne peut pas créer de la bande passante. Elle ne peut pas corriger une saccade de compilation de shader en devinant la prochaine texture. Et elle devient moins utile lorsque le futur du joueur est intentionnellement imprévisible ou lorsque le moteur dispose déjà d'informations de streaming déterministes quasi parfaites.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">Un lien léger avec l'architecture générale de l'IA\u003C\u002Fh2>\n\u003Cp>Le schéma plus profond est similaire aux systèmes de récupération en dehors du jeu : le système ne doit pas tout récupérer, et il ne doit pas attendre le dernier moment pour découvrir que le contexte manque. Il a besoin d'un déclencheur, d'un ensemble de candidats, d'une politique de classement et d'une limite de validité. Le jeu impose simplement un budget de latence beaucoup plus serré.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">Qu'est-ce que le RAG ? L'explication la plus simple de son fonctionnement\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Une explication générale de la récupération, du contexte et de pourquoi les systèmes récupèrent des informations au lieu de tout placer dans le modèle en une fois.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire l'explication du RAG sur stajic.de →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-36\">Source principale\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Préchargement de données basé sur l'apprentissage profond dans la mémoire virtuelle unifiée CPU-GPU\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Article de recherche rapportant les résultats de préchargement de pages UVM appris sur 11 benchmarks gourmands en mémoire.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">AAAI 2026 — Préchargement asynchrone du cache KV\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Preuves publiées en 2026 pour l'utilisation proactive de la bande passante mémoire inactive et du préchargement du cache L2.\u003C\u002Fp>\u003C\u002Fa>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Mémoire de jeu prédictive\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Le préchargement par IA est-il déjà une fonctionnalité standard des moteurs de jeu ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Pas dans l'architecture spécifique Figure Rocks proposée ici. Les jeux utilisent déjà de nombreuses formes de préchargement et de streaming prédictif ; l'hypothèse est de combiner l'état déterministe du jeu avec une prédiction apprise de la mémoire GPU.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Cela augmenterait-il la vitesse de la RAM ou de la VRAM ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Non. Il vise à améliorer quand et quelles données sont déplacées afin que le processeur attende moins souvent.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq3\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Cela pourrait-il éliminer les saccades ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Non. Il ne peut traiter que les blocages causés par la disponibilité\u002Frésidence des données lorsque la prédiction est suffisamment précise.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Pourquoi les jeux pourraient-ils être une bonne cible de prédiction ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Parce que les moteurs exposent des informations structurées sur la caméra, la topologie du niveau, les assets, l'animation et la visibilité probable qu'une application générique ne fournit pas.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq5\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Quelle métrique compte le plus ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Pas une seule métrique. Une évaluation utile nécessite le taux de réussite des pages\u002Fressources, la surcharge de bande passante, la pollution du cache et la latence de queue du temps de frame.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Termes de mémoire prédictive\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"prefetch\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Préchargement\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Déplacer ou rendre des données disponibles avant une demande explicite.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"residency\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Résidence\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Garder la ressource en mémoire suffisamment proche du processeur pour qu'elle puisse être utilisée sans transfert coûteux.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"cache-pollution\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Pollution du cache\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Remplacer des données utiles par des données préchargées qui s'avèrent inutiles.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"predictive-gaming-memory\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Mémoire de jeu prédictive\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Hypothèse Figure Rocks combinant l'état déterministe du moteur, la prédiction apprise, le préchargement et une politique de résidence mesurée.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>",{"time":538,"blocks":539,"version":1032},1792339220562,[540,546,554,561,568,574,619,628,633,638,661,666,671,707,714,719,724,751,758,763,830,835,840,845,850,858,863,868,900,905,932,937,942,947,952,960,965,975,984,1011],{"id":541,"data":542,"type":544,"tunes":545},"p-0075",{"text":543},"Un jeu connaît rarement l'avenir exact, mais il en sait bien plus sur les prochaines images qu'une application généraliste. Il connaît le vecteur de caméra, la vélocité du joueur, les cellules visibles, les portails à proximité, les dépendances des ressources, l'état de l'animation et souvent quels shaders ou effets sont susceptibles de s'exécuter ensuite. Cela fait du jeu vidéo une cible particulièrement intéressante pour la gestion prédictive de la mémoire.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"c-0076",{"body":549,"title":550,"variant":551},"\u003Cstrong>Le préchargement assisté par IA pourrait plausiblement réduire certains blocages liés à la mémoire, mais ce ne serait pas un bouton anti-saccade universel.\u003C\u002Fstrong> La recherche montre déjà que les préchargeurs à apprentissage profond améliorent la prédiction des pages en mémoire unifiée CPU-GPU. Un moteur de jeu pourrait ajouter ce que ces benchmarks n'ont pas : un état explicite du monde et du joueur. L'hypothèse Figure Rocks est de combiner les deux.","Réponse directe","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"c-0077",{"body":557,"title":558,"variant":559},"Le terme \u003Cstrong>Predictive Gaming Memory\u003C\u002Fstrong> dans cet article décrit une architecture proposée : connaissance déterministe du moteur + prédiction d'accès apprise + préchargement + politique de résidence. Ce n'est pas un nom de produit NVIDIA, Microsoft, Epic ou AMD.","Hypothèse Figure Rocks, pas une fonctionnalité annoncée","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc-0078",{"title":564,"maxLevel":565,"minLevel":47},"Sommaire",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-0079",{"text":571,"level":47},"Séparer d'abord les blocages mémoire des autres saccades","header",{},{"id":575,"data":576,"type":617,"tunes":618},"cmp-0080",{"rows":577,"title":608,"layout":609,"columns":610},[578,584,590,596,602],{"id":579,"label":580,"values":581},"asset","Ressource absente de la mémoire rapide",{"other":582,"memory":583},"Le stockage ou la décompression peuvent aussi être impliqués","Possible",{"id":585,"label":586,"values":587},"shader","Shader non prêt",{"other":588,"memory":589},"La compilation\u002Fcréation de pipeline peut être la vraie cause","Peut impliquer des caches",{"id":591,"label":592,"values":593},"cpu","Pic de simulation CPU",{"other":594,"memory":595},"La logique de jeu ou le travail de draw-call peut dominer","Le comportement du cache\u002Fmémoire peut contribuer",{"id":597,"label":598,"values":599},"network","À-coup en ligne",{"other":600,"memory":601},"Le serveur ou le timing des paquets peut dominer","Généralement pas la cause principale",{"id":603,"label":604,"values":605},"vram","Pression d'éviction\u002Frésidence",{"other":606,"memory":607},"Peut être mal diagnostiquée à partir des compteurs d'allocation seuls","Directement pertinent","Sources de saccades qui peuvent se ressembler","table",[611,614],{"id":612,"label":613},"memory","Chemin mémoire\u002Fdonnées",{"id":615,"label":616},"other","Autre chemin","comparison",{},{"id":620,"data":621,"type":626,"tunes":627},"ref-0081",{"url":622,"title":623,"excerpt":624,"ctaLabel":625},"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it","Pourquoi les jeux PC saccadent lors de la compilation des shaders — et comment la livraison avancée de shaders change la donne","Les saccades de shaders sont un problème différent de la prédiction des pages mémoire et ne doivent pas être regroupées dans un seul diagnostic.","Séparer les saccades de shaders des blocages mémoire","referralArticle",{},{"id":629,"data":630,"type":572,"tunes":632},"h-0082",{"text":631,"level":47},"Le préchargement existe déjà ; la nouvelle question est comment il prédit",{},{"id":634,"data":635,"type":544,"tunes":637},"p-0083",{"text":636},"Le préchargement traditionnel tente de déplacer les données avant qu'un processeur ne les demande explicitement. Les préchargeurs matériels exploitent les motifs d'accès. Les moteurs diffusent les cellules du monde avant que le joueur ne les atteigne. Les systèmes d'exploitation et les runtimes gardent chaudes les pages probablement nécessaires. La partie difficile est la fausse prédiction : précharger trop peu et vous bloquez encore ; précharger trop et vous gaspillez de la bande passante et évincez des données utiles.",{},{"id":639,"data":640,"type":659,"tunes":660},"flow-0084",{"steps":641,"title":657,"orientation":658},[642,645,648,651,654],{"label":643,"description":644},"Observer","Collecter les accès récents, l'état du monde et le contexte de charge de travail.",{"label":646,"description":647},"Prédire","Estimer quelles pages\u002Fressources\u002Fshaders seront nécessaires ensuite.",{"label":649,"description":650},"Déplacer","Transférer ou rendre résidentes ces ressources avant la demande.",{"label":652,"description":653},"Utiliser","Le GPU consomme la ressource sans attendre si la prédiction était correcte.",{"label":655,"description":656},"Corriger","Évincer les mauvaises prédictions et mettre à jour le modèle\u002Fla politique.","Le compromis de base du préchargement","auto","processFlow",{},{"id":662,"data":663,"type":572,"tunes":665},"h-0085",{"text":664,"level":47},"Il existe déjà des preuves de recherche pour le préchargement GPU appris",{},{"id":667,"data":668,"type":544,"tunes":670},"p-0086",{"text":669},"Une étude publiée sur le préchargement basé sur l'apprentissage profond pour la mémoire virtuelle unifiée CPU-GPU a utilisé un Transformer pour modéliser le contexte global d'accès mémoire, puis en a dérivé un prédicteur plus léger. Sur 11 benchmarks intensifs en mémoire, l'article a rapporté une amélioration de performance de 10,89 % par rapport au framework comparé, un taux de succès des pages en mémoire de périphérique de 89,02 % contre 76,10 %, et 11,05 % de trafic d'interconnexion CPU-GPU en moins.",{},{"id":672,"data":673,"type":609,"tunes":706},"t-0087",{"content":674,"stretched":705,"withHeadings":15},[675,680,685,690,695,700],[676,677,678,679],"Résultat de recherche","Valeur rapportée","Ce que cela soutient","Ce que cela ne prouve PAS",[681,682,683,684],"Performance UVM apprise","+10,89 %","La prédiction d'accès apprise peut battre une ligne de base de préchargement UVM antérieure","Un jeu commercial gagne 10,89 % de FPS",[686,687,688,689],"Taux de succès des pages UVM","89,02 % vs 76,10 %","La prédiction peut modifier matériellement l'endroit où les données demandées sont trouvées","Chaque texture\u002Fpage de jeu est également prévisible",[691,692,693,694],"Trafic d'interconnexion CPU–GPU","−11,05 %","Un meilleur placement peut réduire les mouvements réactifs","Le trafic PCIe diminue toujours dans les jeux",[696,697,698,699],"Efficacité du noyau d'attention AAAI","2,15×","Le préchargement peut masquer un accès mémoire coûteux derrière un calcul utile","Les noyaux de jeu reçoivent 2,15×",[701,702,703,704],"Débit de bout en bout AAAI","Jusqu'à 1,97×","Le masquage de latence peut se propager au-delà d'un noyau","Un multiplicateur de fréquence d'images de jeu de 1,97×",false,{},{"id":708,"data":709,"type":552,"tunes":713},"c-0088",{"body":710,"title":711,"variant":712},"Ces résultats proviennent de l'ensemble de benchmarks de l'article, \u003Cstrong>pas de moteurs de jeu commerciaux\u003C\u002Fstrong>. Ils prouvent que la prédiction d'accès mémoire apprise peut fonctionner dans des charges de travail UVM GPU. Ils ne prouvent pas un gain de 10,89 % de FPS ni une réduction spécifique des saccades dans un jeu.","Ne copiez pas ces chiffres sur les jeux","warning",{},{"id":715,"data":716,"type":544,"tunes":718},"v3-two-evidence-p",{"text":717},"L'étude sur l'UVM apprise est utile car elle prédit quel accès de page est probable à partir du comportement mémoire. Le travail AAAI 2026 sur le cache KV est utile pour une autre raison : il montre qu'un ordonnanceur peut exploiter des fenêtres de bande passante autrement inactives pour décider quand déplacer des données vers un cache plus rapide avant la demande. Ensemble, ils soutiennent deux mécanismes distincts dans l'hypothèse Figure Rocks — la prédiction et le timing — bien qu'aucun des deux articles ne soit un benchmark de jeu.",{},{"id":720,"data":721,"type":572,"tunes":723},"h-0089",{"text":722,"level":47},"Les jeux disposent d'informations supplémentaires qu'un préchargeur générique n'a pas",{},{"id":725,"data":726,"type":659,"tunes":750},"flow-0090",{"steps":727,"title":749,"orientation":658},[728,731,734,737,740,743,746],{"label":729,"description":730},"1. Lire l'état déterministe","Direction de caméra, position, vélocité, graphe de niveau, visibilité, animation, état de quête ou de rencontre.",{"label":732,"description":733},"2. Lire l'historique d'accès","Quelles pages de texture, maillages, shaders, clusters de géométrie et données de modèle ont été récemment utilisés.",{"label":735,"description":736},"3. Prédire l'ensemble de travail à court terme","Estimer les ressources probablement nécessaires plusieurs images ou secondes à l'avance selon le coût.",{"label":738,"description":739},"4. Attribuer confiance et coût","Une ressource de 4 Mo à 95 % de probabilité est une décision différente d'une région de 2 Go à 20 % de probabilité.",{"label":741,"description":742},"5. Précharger dans un budget de bande passante","Ne pas laisser la prédiction elle-même saturer le chemin nécessaire au rendu actuel.",{"label":744,"description":745},"6. Mesurer les échecs et la pollution","Suivre les blocages évités, les mauvaises prédictions et les données utiles évincées.",{"label":747,"description":748},"7. S'adapter","Mettre à jour le prédicteur ou la politique du moteur pour la scène et le comportement réels du joueur.","La boucle Predictive Gaming Memory",{},{"id":752,"data":753,"type":552,"tunes":757},"c-0091",{"body":754,"title":755,"variant":756},"Un jeu n'a pas à choisir entre streaming écrit à la main et IA. \u003Cstrong>La conception la plus solide est peut-être hybride :\u003C\u002Fstrong> des règles déterministes garantissent les ressources critiques, tandis qu'un prédicteur appris classe le travail optionnel ou à court terme, choisit la profondeur de préchargement et ne dépense que la marge de bande passante sûre. Si la confiance est faible, le système revient aux règles écrites.","L'hybride intéressant","success",{},{"id":759,"data":760,"type":572,"tunes":762},"v3-known-future",{"text":761,"level":47},"Le moteur peut combiner le futur connu avec le futur prédit",{},{"id":764,"data":765,"type":617,"tunes":829},"v3-known-pred",{"rows":766,"title":821,"layout":609,"columns":822},[767,773,779,785,791,797,803,809,815],{"id":768,"label":769,"values":770},"camera","État de la caméra \u002F du portail",{"known":771,"learned":772},"Vue actuelle, frustum, graphe de salles, portail visible","Dans quelle direction le joueur est susceptible de tourner ensuite",{"id":774,"label":775,"values":776},"movement","Mouvement du joueur",{"known":777,"learned":778},"Vélocité, entrées, contraintes de navigation","Chemin de traversée probable plusieurs instants à l'avance",{"id":780,"label":781,"values":782},"assets","Dépendances des ressources",{"known":783,"learned":784},"Dépendances pour une cellule de niveau ou un objet connu","Quelles ressources voisines optionnelles deviendront sollicitées",{"id":786,"label":787,"values":788},"lod","État du LOD",{"known":789,"learned":790},"Distance et seuils actuels","Quelles transitions de LOD sont probables bientôt",{"id":792,"label":793,"values":794},"shaders","Graphe de matériaux\u002Fshader",{"known":795,"learned":796},"Shaders requis par le contenu visible connu","Quelles variantes sont susceptibles d'être demandées ensuite",{"id":798,"label":799,"values":800},"strength","Force du cœur",{"known":801,"learned":802},"Connaît les règles strictes du niveau, les dépendances et les transitions garanties","Classe l'accès futur ambigu à partir du comportement et du contexte",{"id":804,"label":805,"values":806},"risk","Mode de défaillance",{"known":807,"learned":808},"Peut être conservateur et charger de larges ensembles","Peut se tromper et polluer le cache ou la mémoire",{"id":810,"label":811,"values":812},"best","Meilleur usage",{"known":813,"learned":814},"Ressources critiques et transitions garanties","Priorisation optionnelle dans un budget de bande passante borné",{"id":816,"label":817,"values":818},"safety","Politique de sécurité",{"known":819,"learned":820},"Facile à raisonner et à tester","Nécessite des seuils de confiance, de la télémétrie et un repli déterministe","Connaissance déterministe vs prédiction apprise",[823,826],{"id":824,"label":825},"known","Le moteur sait déjà",{"id":827,"label":828},"learned","Le prédicteur peut estimer",{},{"id":831,"data":832,"type":572,"tunes":834},"h-0092",{"text":833,"level":47},"Un exemple simple : courir vers une porte",{},{"id":836,"data":837,"type":544,"tunes":839},"p-0093",{"text":838},"Imaginez un joueur sprintant dans un couloir vers une porte qui s'ouvre sur une place de marché dense. Un système de streaming conventionnel connaît déjà la zone suivante et peut commencer à la charger. La mémoire prédictive ajoute une seconde couche : en se basant sur la direction de la caméra, le mouvement, les choix antérieurs du joueur et l'état actuel de la mission, elle peut classer quel sous-ensemble de textures, de données d'animation de PNJ, de shaders et de géométrie est le plus susceptible d'être nécessaire en premier.",{},{"id":841,"data":842,"type":572,"tunes":844},"h-0095",{"text":843,"level":47},"Pourquoi cela pourrait compter davantage à mesure que le rendu neuronal se développe",{},{"id":846,"data":847,"type":544,"tunes":849},"p-0096",{"text":848},"Les graphismes neuronaux ajoutent de nouvelles classes de données : poids de modèles, représentations latentes ou compressées, tampons d'historique et tenseurs intermédiaires. Si ces ressources entrent en concurrence avec les textures et la géométrie, la résidence devient plus complexe. La planification prédictive pourrait décider non seulement quelle ressource déplacer, mais aussi quelle représentation conserver : données brutes, représentation neuronale compressée ou forme reconstructible.",{},{"id":851,"data":852,"type":626,"tunes":857},"ref-0097",{"url":853,"title":854,"excerpt":855,"ctaLabel":856},"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 n'est pas qu'un upscaling : ce que le rendu neuronal génératif change réellement","Le rendu neuronal change les données dont une image a besoin et le moment où elle en a besoin.","Lire l'analyse sur le rendu neuronal",{},{"id":859,"data":860,"type":572,"tunes":862},"h-0098",{"text":861,"level":47},"Les modes de défaillance sont aussi importants que l'opportunité",{},{"id":864,"data":865,"type":544,"tunes":867},"v3-bandwidth-budget-p",{"text":866},"Le prefetch n'est pas gratuit. Une mauvaise prédiction peut récupérer des ressources que le joueur ne verra jamais, évincer des pages utiles et entrer en concurrence avec le trafic critique pour l'image. L'objectif correct n'est donc pas le prefetch maximal ; c'est le taux de réussite utile maximal dans un budget de bande passante et de résidence borné.",{},{"id":869,"data":870,"type":609,"tunes":899},"t-0099",{"content":871,"stretched":705,"withHeadings":15},[872,875,879,883,887,891,895],[805,873,874],"Ce qui se passe","Idée d'atténuation",[876,877,878],"Mauvaise prédiction","Des données erronées sont préchargées","Seuils de confiance ; réserver du budget pour la demande déterministe",[880,881,882],"Pollution du cache","Des données actuelles utiles sont évincées","Séparer les classes de résidence prédites et protégées",[884,885,886],"Contention de bande passante","Le prefetch vole de la bande passante à l'image actuelle","Planifier uniquement dans les fenêtres de marge ; imposer un budget par image",[888,889,890],"Changement de comportement","Le joueur fait demi-tour ou se téléporte","Annulation peu coûteuse et repli rapide",[892,893,894],"Surcoût du modèle","Le prédicteur coûte plus qu'il n'économise","Petits modèles, entraînement hors ligne ou prédicteurs légers dérivés de règles",[896,897,898],"Saccade non liée à la mémoire","Le problème persiste","Garder le diagnostic séparé : les chemins shader, CPU, réseau et stockage nécessitent leurs propres tests",{},{"id":901,"data":902,"type":572,"tunes":904},"h-0100",{"text":903,"level":47},"Comment vous le testeriez dans un vrai moteur",{},{"id":906,"data":907,"type":659,"tunes":931},"flow-0101",{"steps":908,"title":930,"orientation":658},[909,912,915,918,921,924,927],{"label":910,"description":911},"A. Établir la référence","Capturer les pics de temps d'image, les défauts de page\u002Fmigrations, les échecs de résidence et le volume de transfert de données sans prédiction.",{"label":913,"description":914},"B. Ajouter uniquement le prefetch déterministe","Mesurer ce que la connaissance conventionnelle du moteur résout déjà.",{"label":916,"description":917},"C. Ajouter le classement appris","Utiliser le même budget de mémoire et de bande passante pour que la comparaison soit équitable.",{"label":919,"description":920},"D. Mesurer le taux de réussite et la pollution","Suivre les octets préchargés utiles par rapport aux octets gaspillés et aux évictions.",{"label":922,"description":923},"E. Mesurer la distribution des temps d'image","Le FPS moyen ne suffit pas ; inspecter la latence de queue et la fréquence des saccades.",{"label":925,"description":926},"F. Tester les changements de comportement","Virages rapides, téléportation, imprévisibilité multijoueur et scènes procédurales.",{"label":928,"description":929},"G. Comparer le coût","Le temps CPU\u002FGPU du prédicteur + le surcoût mémoire doivent être inférieurs aux blocages qu'il élimine.","Évaluation de la mémoire prédictive",{},{"id":933,"data":934,"type":572,"tunes":936},"h-0102",{"text":935,"level":47},"Où cette hypothèse s'arrête",{},{"id":938,"data":939,"type":544,"tunes":941},"p-0103",{"text":940},"La mémoire prédictive ne peut pas rendre infiniment grand un pool de mémoire trop petit. Elle ne peut pas créer de la bande passante. Elle ne peut pas corriger une saccade de compilation de shader en devinant la prochaine texture. Et elle devient moins utile lorsque le futur du joueur est intentionnellement imprévisible ou lorsque le moteur dispose déjà d'informations de streaming déterministes quasi parfaites.",{},{"id":943,"data":944,"type":572,"tunes":946},"h-0104",{"text":945,"level":47},"Un lien léger avec l'architecture générale de l'IA",{},{"id":948,"data":949,"type":544,"tunes":951},"p-0105",{"text":950},"Le schéma plus profond est similaire aux systèmes de récupération en dehors du jeu : le système ne doit pas tout récupérer, et il ne doit pas attendre le dernier moment pour découvrir que le contexte manque. Il a besoin d'un déclencheur, d'un ensemble de candidats, d'une politique de classement et d'une limite de validité. Le jeu impose simplement un budget de latence beaucoup plus serré.",{},{"id":953,"data":954,"type":626,"tunes":959},"ref-0106",{"url":955,"title":956,"excerpt":957,"ctaLabel":958},"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","Qu'est-ce que le RAG ? L'explication la plus simple de son fonctionnement","Une explication générale de la récupération, du contexte et de pourquoi les systèmes récupèrent des informations au lieu de tout placer dans le modèle en une fois.","Lire l'explication du RAG sur stajic.de",{},{"id":961,"data":962,"type":572,"tunes":964},"h-0107",{"text":963,"level":47},"Source principale",{},{"id":966,"data":967,"type":973,"tunes":974},"src-0108",{"link":968,"meta":969},"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490",{"image":970,"title":971,"description":972},{"url":13},"Préchargement de données basé sur l'apprentissage profond dans la mémoire virtuelle unifiée CPU-GPU","Article de recherche rapportant les résultats de préchargement de pages UVM appris sur 11 benchmarks gourmands en mémoire.","linkTool",{},{"id":976,"data":977,"type":973,"tunes":983},"v3-src-aaai2",{"link":978,"meta":979},"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224",{"image":980,"title":981,"description":982},{"url":13},"AAAI 2026 — Préchargement asynchrone du cache KV","Preuves publiées en 2026 pour l'utilisation proactive de la bande passante mémoire inactive et du préchargement du cache L2.",{},{"id":985,"data":986,"type":1009,"tunes":1010},"faq-0109",{"items":987,"title":1008},[988,992,996,1000,1004],{"id":989,"answer":990,"question":991},"faq1","Pas dans l'architecture spécifique Figure Rocks proposée ici. Les jeux utilisent déjà de nombreuses formes de préchargement et de streaming prédictif ; l'hypothèse est de combiner l'état déterministe du jeu avec une prédiction apprise de la mémoire GPU.","Le préchargement par IA est-il déjà une fonctionnalité standard des moteurs de jeu ?",{"id":993,"answer":994,"question":995},"faq2","Non. Il vise à améliorer quand et quelles données sont déplacées afin que le processeur attende moins souvent.","Cela augmenterait-il la vitesse de la RAM ou de la VRAM ?",{"id":997,"answer":998,"question":999},"faq3","Non. Il ne peut traiter que les blocages causés par la disponibilité\u002Frésidence des données lorsque la prédiction est suffisamment précise.","Cela pourrait-il éliminer les saccades ?",{"id":1001,"answer":1002,"question":1003},"faq4","Parce que les moteurs exposent des informations structurées sur la caméra, la topologie du niveau, les assets, l'animation et la visibilité probable qu'une application générique ne fournit pas.","Pourquoi les jeux pourraient-ils être une bonne cible de prédiction ?",{"id":1005,"answer":1006,"question":1007},"faq5","Pas une seule métrique. Une évaluation utile nécessite le taux de réussite des pages\u002Fressources, la surcharge de bande passante, la pollution du cache et la latence de queue du temps de frame.","Quelle métrique compte le plus ?","Mémoire de jeu prédictive","faq",{},{"id":1012,"data":1013,"type":1030,"tunes":1031},"gloss-0110",{"title":1014,"entries":1015},"Termes de mémoire prédictive",[1016,1020,1024,1027],{"term":1017,"anchor":1018,"definition":1019},"Préchargement","prefetch","Déplacer ou rendre des données disponibles avant une demande explicite.",{"term":1021,"anchor":1022,"definition":1023},"Résidence","residency","Garder la ressource en mémoire suffisamment proche du processeur pour qu'elle puisse être utilisée sans transfert coûteux.",{"term":880,"anchor":1025,"definition":1026},"cache-pollution","Remplacer des données utiles par des données préchargées qui s'avèrent inutiles.",{"term":1008,"anchor":1028,"definition":1029},"predictive-gaming-memory","Hypothèse Figure Rocks combinant l'état déterministe du moteur, la prédiction apprise, le préchargement et une politique de résidence mesurée.","glossary",{},"2.31","Les moteurs de jeu savent déjà où vont le joueur, la caméra et le monde. La recherche montre que le préchargement GPU appris peut prédire les pages mémoire. Que se passe-t-il si ces idées sont combinées ?","\u002Fuploads\u002F2026\u002F10\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter-1792339100514-lyx0yw.webp","what-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter-1792339100514-lyx0yw","PUBLISHED","2026-10-18T11:57:00.000Z","2026-10-18T15:57:27.289Z","2026-10-18T16:01:40.667Z",{"en":1041,"de":1042,"sr":1043,"es":1044,"fr":1045,"it":1046,"ru":1047,"zh":1048},"\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fde\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fsr\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fes\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Ffr\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fit\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fru\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter","\u002Fzh\u002Fblog\u002Fwhat-if-a-game-could-predict-its-next-memory-access-ai-prefetching-and-the-future-of-stutter",[1050,1053,1057,1061,1065,1069,1073],{"id":1051,"name":286,"slug":1052},144,"tech",{"id":1054,"name":1055,"slug":1056},170,"Actualités Tech","tech-news",{"id":1058,"name":1059,"slug":1060},280,"Chronologie des technologies d'affichage","display-tech-timeline",{"id":1062,"name":1063,"slug":1064},153,"Technologie d'affichage","display-tech",{"id":1066,"name":1067,"slug":1068},165,"Actualités","news",{"id":1070,"name":1071,"slug":1072},166,"Actualités Gaming","gaming-news",{"id":1074,"name":1075,"slug":1076},427,"Nouveautés","new",{"id":283,"login":1078,"email":13,"displayName":1079},"aleksandar","Aleksandar Stajic",[1081,1493],{"lang":8,"title":1082,"content":1083,"contentJson":1084,"excerpt":1492},"What If a Game Could Predict Its Next Memory Access? AI Prefetching and the Future of Stutter","{\"time\":1792339102894,\"blocks\":[{\"id\":\"p-0075\",\"type\":\"paragraph\",\"data\":{\"text\":\"A game rarely knows the exact future, but it knows much more about the next few frames than a general-purpose application does. It knows the camera vector, player velocity, visible cells, nearby portals, asset dependencies, animation state and often which shaders or effects are likely to run next. That makes gaming an unusually interesting target for predictive memory management.\"},\"tunes\":{}},{\"id\":\"c-0076\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>AI-assisted prefetching could plausibly reduce some memory-related stalls, but it would not be a universal anti-stutter button.\u003C\u002Fstrong> Research already shows deep-learning prefetchers improving CPU-GPU unified-memory page prediction. A game engine could add something those benchmarks do not have: explicit world and player state. The Figure Rocks hypothesis is to combine both.\"},\"tunes\":{}},{\"id\":\"c-0077\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Figure Rocks hypothesis, not an announced feature\",\"body\":\"The term \u003Cstrong>Predictive Gaming Memory\u003C\u002Fstrong> in this article describes a proposed architecture: deterministic engine knowledge + learned access prediction + prefetch + residency policy. It is not an NVIDIA, Microsoft, Epic or AMD product name.\"},\"tunes\":{}},{\"id\":\"toc-0078\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-0079\",\"type\":\"header\",\"data\":{\"text\":\"First separate memory stalls from other stutter\",\"level\":2},\"tunes\":{}},{\"id\":\"cmp-0080\",\"type\":\"comparison\",\"data\":{\"title\":\"Stutter sources that can look similar\",\"layout\":\"table\",\"columns\":[{\"id\":\"memory\",\"label\":\"Memory\u002Fdata path\"},{\"id\":\"other\",\"label\":\"Other path\"}],\"rows\":[{\"id\":\"asset\",\"label\":\"Asset missing from fast memory\",\"values\":{\"memory\":\"Possible\",\"other\":\"Storage or decompression can also be involved\"}},{\"id\":\"shader\",\"label\":\"Shader not ready\",\"values\":{\"memory\":\"May involve caches\",\"other\":\"Compilation\u002Fpipeline creation can be the real cause\"}},{\"id\":\"cpu\",\"label\":\"CPU simulation spike\",\"values\":{\"memory\":\"Cache\u002Fmemory behavior can contribute\",\"other\":\"Game logic or draw-call work may dominate\"}},{\"id\":\"network\",\"label\":\"Online hitch\",\"values\":{\"memory\":\"Usually not the primary cause\",\"other\":\"Server or packet timing can dominate\"}},{\"id\":\"vram\",\"label\":\"Eviction\u002Fresidency pressure\",\"values\":{\"memory\":\"Directly relevant\",\"other\":\"Can be misdiagnosed from allocation meters alone\"}}]},\"tunes\":{}},{\"id\":\"ref-0081\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it\",\"title\":\"Why PC Games Stutter When Compiling Shaders — and How Advanced Shader Delivery Changes It\",\"excerpt\":\"Shader stutter is a different problem from memory-page prediction and should not be collapsed into one diagnosis.\",\"ctaLabel\":\"Separate shader stutter from memory stalls\"},\"tunes\":{}},{\"id\":\"h-0082\",\"type\":\"header\",\"data\":{\"text\":\"Prefetching already exists; the new question is how it predicts\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0083\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional prefetching tries to move data before a processor explicitly asks for it. Hardware prefetchers exploit access patterns. Engines stream world cells before the player reaches them. Operating systems and runtimes keep likely-needed pages hot. The difficult part is false prediction: prefetch too little and you still stall; prefetch too much and you waste bandwidth and evict useful data.\"},\"tunes\":{}},{\"id\":\"flow-0084\",\"type\":\"processFlow\",\"data\":{\"title\":\"The basic prefetch trade-off\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Observe\",\"description\":\"Collect recent accesses, world state and workload context.\"},{\"label\":\"Predict\",\"description\":\"Estimate which pages\u002Fassets\u002Fshaders will be needed next.\"},{\"label\":\"Move\",\"description\":\"Transfer or make those resources resident before demand.\"},{\"label\":\"Use\",\"description\":\"The GPU consumes the resource without waiting if prediction was correct.\"},{\"label\":\"Correct\",\"description\":\"Evict wrong predictions and update the model\u002Fpolicy.\"}]},\"tunes\":{}},{\"id\":\"h-0085\",\"type\":\"header\",\"data\":{\"text\":\"There is already research evidence for learned GPU prefetching\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0086\",\"type\":\"paragraph\",\"data\":{\"text\":\"A published study on deep-learning-based prefetching for CPU-GPU Unified Virtual Memory used a Transformer to model global memory-access context and then derived a lighter predictor. Across 11 memory-intensive benchmarks, the paper reported 10.89% performance improvement over the compared framework, a device-memory page hit rate of 89.02% versus 76.10%, and 11.05% lower CPU-GPU interconnect traffic.\"},\"tunes\":{}},{\"id\":\"t-0087\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Research result\",\"Reported value\",\"What it supports\",\"What it does NOT prove\"],[\"Learned UVM performance\",\"+10.89%\",\"Learned access prediction can beat a prior UVM prefetch baseline\",\"A commercial game gains 10.89% FPS\"],[\"UVM page hit rate\",\"89.02% vs 76.10%\",\"Prediction can materially change where requested data is found\",\"Every game texture\u002Fpage is equally predictable\"],[\"CPU–GPU interconnect traffic\",\"−11.05%\",\"Better placement can reduce reactive movement\",\"PCIe traffic always falls in games\"],[\"AAAI attention-kernel efficiency\",\"2.15×\",\"Prefetch can hide expensive memory access behind useful compute\",\"Gaming kernels receive 2.15×\"],[\"AAAI end-to-end throughput\",\"Up to 1.97×\",\"Latency hiding can propagate beyond one kernel\",\"A 1.97× game frame-rate multiplier\"]]},\"tunes\":{}},{\"id\":\"c-0088\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"Do not copy these numbers onto games\",\"body\":\"Those results come from the paper's benchmark set, \u003Cstrong>not from commercial game engines\u003C\u002Fstrong>. They prove that learned memory-access prediction can work in GPU UVM workloads. They do not prove a 10.89% FPS gain or a specific stutter reduction in a game.\"},\"tunes\":{}},{\"id\":\"v3-two-evidence-p\",\"type\":\"paragraph\",\"data\":{\"text\":\"The learned-UVM study is useful because it predicts what page access is likely from memory behavior. The 2026 AAAI KV-cache work is useful for a different reason: it shows that a scheduler can exploit otherwise idle bandwidth windows to decide when to move data into a faster cache before demand. Together they support two separate mechanisms in the Figure Rocks hypothesis — prediction and timing — while neither paper is a gaming benchmark.\"},\"tunes\":{}},{\"id\":\"h-0089\",\"type\":\"header\",\"data\":{\"text\":\"Games have extra information a generic prefetcher does not\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0090\",\"type\":\"processFlow\",\"data\":{\"title\":\"The Predictive Gaming Memory Loop\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Read deterministic state\",\"description\":\"Camera direction, position, velocity, level graph, visibility, animation, quest or encounter state.\"},{\"label\":\"2. Read access history\",\"description\":\"Which texture pages, meshes, shaders, geometry clusters and model data were recently used.\"},{\"label\":\"3. Predict the near-future working set\",\"description\":\"Estimate likely-needed resources several frames or seconds ahead depending on cost.\"},{\"label\":\"4. Assign confidence and cost\",\"description\":\"A 95% likely 4 MB asset is a different decision from a 20% likely 2 GB region.\"},{\"label\":\"5. Prefetch within a bandwidth budget\",\"description\":\"Do not let prediction itself saturate the path needed by current rendering.\"},{\"label\":\"6. Measure misses and pollution\",\"description\":\"Track stalls avoided, wrong predictions and useful data evicted.\"},{\"label\":\"7. Adapt\",\"description\":\"Update the predictor or engine policy for the actual scene and player behavior.\"}]},\"tunes\":{}},{\"id\":\"c-0091\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The interesting hybrid\",\"body\":\"A game does not have to choose between hand-authored streaming and AI. \u003Cstrong>The strongest design may be hybrid:\u003C\u002Fstrong> deterministic rules guarantee critical resources, while a learned predictor ranks optional or near-future work, chooses prefetch depth and spends only safe bandwidth headroom. If confidence is low, the system falls back to authored rules.\"},\"tunes\":{}},{\"id\":\"v3-known-future\",\"type\":\"header\",\"data\":{\"text\":\"The engine can combine known future with predicted future\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-known-pred\",\"type\":\"comparison\",\"data\":{\"title\":\"Deterministic knowledge vs learned prediction\",\"layout\":\"table\",\"columns\":[{\"id\":\"known\",\"label\":\"Engine already knows\"},{\"id\":\"learned\",\"label\":\"Predictor can estimate\"}],\"rows\":[{\"id\":\"camera\",\"label\":\"Camera \u002F portal state\",\"values\":{\"known\":\"Current view, frustum, room graph, visible portal\",\"learned\":\"Which direction the player is likely to turn next\"}},{\"id\":\"movement\",\"label\":\"Player movement\",\"values\":{\"known\":\"Velocity, input, navigation constraints\",\"learned\":\"Likely traversal path several moments ahead\"}},{\"id\":\"assets\",\"label\":\"Asset dependencies\",\"values\":{\"known\":\"Dependencies for a known level cell or object\",\"learned\":\"Which optional neighboring assets will become hot\"}},{\"id\":\"lod\",\"label\":\"LOD state\",\"values\":{\"known\":\"Current distance and thresholds\",\"learned\":\"Which LOD transitions are likely soon\"}},{\"id\":\"shaders\",\"label\":\"Material\u002Fshader graph\",\"values\":{\"known\":\"Shaders required by known visible content\",\"learned\":\"Which variants are likely to be requested next\"}},{\"id\":\"strength\",\"label\":\"Core strength\",\"values\":{\"known\":\"Knows hard level rules, dependencies and guaranteed transitions\",\"learned\":\"Ranks ambiguous future access from behavior and context\"}},{\"id\":\"risk\",\"label\":\"Failure mode\",\"values\":{\"known\":\"Can be conservative and load broad sets\",\"learned\":\"Can be wrong and pollute cache or memory\"}},{\"id\":\"best\",\"label\":\"Best use\",\"values\":{\"known\":\"Critical assets and guaranteed transitions\",\"learned\":\"Optional prioritization inside a bounded bandwidth budget\"}},{\"id\":\"safety\",\"label\":\"Safety policy\",\"values\":{\"known\":\"Easy to reason about and test\",\"learned\":\"Needs confidence thresholds, telemetry and deterministic fallback\"}}]},\"tunes\":{}},{\"id\":\"h-0092\",\"type\":\"header\",\"data\":{\"text\":\"A simple example: running toward a door\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0093\",\"type\":\"paragraph\",\"data\":{\"text\":\"Imagine a player sprinting down a corridor toward a door that opens into a dense market square. A conventional streaming system already knows the next zone and can begin loading it. Predictive memory adds a second layer: based on camera direction, movement, prior player choices and current mission state, it can rank which subset of textures, NPC animation data, shaders and geometry is most likely to be needed first.\"},\"tunes\":{}},{\"id\":\"h-0095\",\"type\":\"header\",\"data\":{\"text\":\"Why this could matter more as neural rendering grows\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0096\",\"type\":\"paragraph\",\"data\":{\"text\":\"Neural graphics adds new data classes: model weights, latent or compressed representations, history buffers and intermediate tensors. If those resources compete with textures and geometry, residency becomes more complicated. Predictive scheduling could decide not only which asset to move, but which representation to keep: raw data, a compressed neural representation, or a reconstructable form.\"},\"tunes\":{}},{\"id\":\"ref-0097\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\",\"title\":\"DLSS 5 Is Not Just Upscaling: What Generative Neural Rendering Actually Changes\",\"excerpt\":\"Neural rendering changes what data a frame needs and when it needs it.\",\"ctaLabel\":\"Read the neural rendering analysis\"},\"tunes\":{}},{\"id\":\"h-0098\",\"type\":\"header\",\"data\":{\"text\":\"The failure modes are as important as the opportunity\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-bandwidth-budget-p\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prefetch is not free. A wrong prediction can fetch resources the player never sees, evict useful pages and compete with frame-critical traffic. The correct goal is therefore not maximum prefetch; it is maximum useful hit rate inside a bounded bandwidth and residency budget.\"},\"tunes\":{}},{\"id\":\"t-0099\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Failure mode\",\"What happens\",\"Mitigation idea\"],[\"Bad prediction\",\"Wrong data is prefetched\",\"Confidence thresholds; reserve budget for deterministic demand\"],[\"Cache pollution\",\"Useful current data is evicted\",\"Separate predicted and protected residency classes\"],[\"Bandwidth contention\",\"Prefetch steals bandwidth from current frame\",\"Schedule only in headroom windows; enforce per-frame budget\"],[\"Behavior shift\",\"Player turns around or fast-travels\",\"Cheap cancellation and rapid fallback\"],[\"Model overhead\",\"Predictor costs more than it saves\",\"Small models, offline training, or rule-derived lightweight predictors\"],[\"Non-memory stutter\",\"Problem remains\",\"Keep diagnosis separate: shader, CPU, network and storage paths need their own tests\"]]},\"tunes\":{}},{\"id\":\"h-0100\",\"type\":\"header\",\"data\":{\"text\":\"How you would test it in a real engine\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0101\",\"type\":\"processFlow\",\"data\":{\"title\":\"Predictive Memory Evaluation\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"A. Establish baseline\",\"description\":\"Capture frame-time spikes, page faults\u002Fmigrations, residency misses and data-transfer volume without prediction.\"},{\"label\":\"B. Add deterministic prefetch only\",\"description\":\"Measure what conventional engine knowledge already solves.\"},{\"label\":\"C. Add learned ranking\",\"description\":\"Use the same memory and bandwidth budget so the comparison is fair.\"},{\"label\":\"D. Measure hit rate and pollution\",\"description\":\"Track useful prefetched bytes versus wasted bytes and evictions.\"},{\"label\":\"E. Measure frame-time distribution\",\"description\":\"Average FPS is not enough; inspect tail latency and hitch frequency.\"},{\"label\":\"F. Stress behavior changes\",\"description\":\"Fast turns, teleport, multiplayer unpredictability and procedural scenes.\"},{\"label\":\"G. Compare cost\",\"description\":\"Predictor CPU\u002FGPU time + memory overhead must be lower than the stalls it removes.\"}]},\"tunes\":{}},{\"id\":\"h-0102\",\"type\":\"header\",\"data\":{\"text\":\"Where this hypothesis stops\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0103\",\"type\":\"paragraph\",\"data\":{\"text\":\"Predictive memory cannot make a too-small memory pool infinitely large. It cannot create bandwidth. It cannot fix a shader compilation hitch by guessing the next texture. And it becomes less useful when the player's future is intentionally unpredictable or when the engine already has near-perfect deterministic streaming information.\"},\"tunes\":{}},{\"id\":\"h-0104\",\"type\":\"header\",\"data\":{\"text\":\"A light connection to general AI architecture\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0105\",\"type\":\"paragraph\",\"data\":{\"text\":\"The deeper pattern is similar to retrieval systems outside gaming: the system should not fetch everything, and it should not wait until the final moment to discover that context is missing. It needs a trigger, a candidate set, a ranking policy and a validity boundary. Gaming simply imposes a much tighter latency budget.\"},\"tunes\":{}},{\"id\":\"ref-0106\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works\",\"title\":\"What Is RAG? The Simplest Explanation of How It Works\",\"excerpt\":\"A general explanation of retrieval, context and why systems fetch information instead of placing everything in the model at once.\",\"ctaLabel\":\"Read the RAG explainer on stajic.de\"},\"tunes\":{}},{\"id\":\"h-0107\",\"type\":\"header\",\"data\":{\"text\":\"Primary source\",\"level\":2},\"tunes\":{}},{\"id\":\"src-0108\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\",\"meta\":{\"title\":\"Deep learning based data prefetching in CPU-GPU Unified Virtual Memory\",\"description\":\"Research paper reporting learned UVM page-prefetching results across 11 memory-intensive benchmarks.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v3-src-aaai2\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\",\"meta\":{\"title\":\"AAAI 2026 — Asynchronous KV Cache Prefetching\",\"description\":\"Published 2026 evidence for proactively using idle memory bandwidth and L2 cache prefetch.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"faq-0109\",\"type\":\"faq\",\"data\":{\"title\":\"Predictive gaming memory\",\"items\":[{\"id\":\"faq1\",\"question\":\"Is AI prefetching already a standard game-engine feature?\",\"answer\":\"Not in the specific Figure Rocks architecture proposed here. Games already use many forms of prefetching and predictive streaming; the hypothesis is to combine deterministic game state with learned GPU-memory prediction.\"},{\"id\":\"faq2\",\"question\":\"Would this increase RAM or VRAM speed?\",\"answer\":\"No. It aims to improve when and what data is moved so the processor waits less often.\"},{\"id\":\"faq3\",\"question\":\"Could it eliminate stutter?\",\"answer\":\"No. It can only address stalls caused by data availability\u002Fresidency when prediction is accurate enough.\"},{\"id\":\"faq4\",\"question\":\"Why might games be a good prediction target?\",\"answer\":\"Because engines expose structured information about camera, level topology, assets, animation and likely visibility that a generic application does not.\"},{\"id\":\"faq5\",\"question\":\"What metric matters most?\",\"answer\":\"Not one metric. Useful evaluation needs page\u002Fresource hit rate, bandwidth overhead, cache pollution and frame-time tail latency.\"}]},\"tunes\":{}},{\"id\":\"gloss-0110\",\"type\":\"glossary\",\"data\":{\"title\":\"Predictive memory terms\",\"entries\":[{\"term\":\"Prefetch\",\"definition\":\"Moving or making data available before explicit demand.\",\"anchor\":\"prefetch\"},{\"term\":\"Residency\",\"definition\":\"Keeping the resource in memory close enough to the processor that it can be used without an expensive transfer.\",\"anchor\":\"residency\"},{\"term\":\"Cache pollution\",\"definition\":\"Replacing useful data with prefetched data that turns out not to be needed.\",\"anchor\":\"cache-pollution\"},{\"term\":\"Predictive Gaming Memory\",\"definition\":\"Figure Rocks hypothesis combining deterministic engine state, learned prediction, prefetch and measured residency policy.\",\"anchor\":\"predictive-gaming-memory\"}]},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1085,"blocks":1086,"version":1491},1792339102894,[1087,1091,1096,1101,1105,1109,1143,1150,1154,1158,1178,1182,1186,1220,1225,1229,1233,1259,1264,1268,1323,1327,1331,1335,1339,1346,1350,1354,1385,1389,1415,1419,1423,1427,1431,1437,1441,1448,1455,1475],{"id":541,"data":1088,"type":544,"tunes":1090},{"text":1089},"A game rarely knows the exact future, but it knows much more about the next few frames than a general-purpose application does. It knows the camera vector, player velocity, visible cells, nearby portals, asset dependencies, animation state and often which shaders or effects are likely to run next. That makes gaming an unusually interesting target for predictive memory management.",{},{"id":547,"data":1092,"type":552,"tunes":1095},{"body":1093,"title":1094,"variant":551},"\u003Cstrong>AI-assisted prefetching could plausibly reduce some memory-related stalls, but it would not be a universal anti-stutter button.\u003C\u002Fstrong> Research already shows deep-learning prefetchers improving CPU-GPU unified-memory page prediction. A game engine could add something those benchmarks do not have: explicit world and player state. The Figure Rocks hypothesis is to combine both.","Direct answer",{},{"id":555,"data":1097,"type":552,"tunes":1100},{"body":1098,"title":1099,"variant":559},"The term \u003Cstrong>Predictive Gaming Memory\u003C\u002Fstrong> in this article describes a proposed architecture: deterministic engine knowledge + learned access prediction + prefetch + residency policy. It is not an NVIDIA, Microsoft, Epic or AMD product name.","Figure Rocks hypothesis, not an announced feature",{},{"id":562,"data":1102,"type":566,"tunes":1104},{"title":1103,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1106,"type":572,"tunes":1108},{"text":1107,"level":47},"First separate memory stalls from other stutter",{},{"id":575,"data":1110,"type":617,"tunes":1142},{"rows":1111,"title":1136,"layout":609,"columns":1137},[1112,1116,1121,1126,1131],{"id":579,"label":1113,"values":1114},"Asset missing from fast memory",{"other":1115,"memory":583},"Storage or decompression can also be involved",{"id":585,"label":1117,"values":1118},"Shader not ready",{"other":1119,"memory":1120},"Compilation\u002Fpipeline creation can be the real cause","May involve caches",{"id":591,"label":1122,"values":1123},"CPU simulation spike",{"other":1124,"memory":1125},"Game logic or draw-call work may dominate","Cache\u002Fmemory behavior can contribute",{"id":597,"label":1127,"values":1128},"Online hitch",{"other":1129,"memory":1130},"Server or packet timing can dominate","Usually not the primary cause",{"id":603,"label":1132,"values":1133},"Eviction\u002Fresidency pressure",{"other":1134,"memory":1135},"Can be misdiagnosed from allocation meters alone","Directly relevant","Stutter sources that can look similar",[1138,1140],{"id":612,"label":1139},"Memory\u002Fdata path",{"id":615,"label":1141},"Other path",{},{"id":620,"data":1144,"type":626,"tunes":1149},{"url":1145,"title":1146,"excerpt":1147,"ctaLabel":1148},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it","Why PC Games Stutter When Compiling Shaders — and How Advanced Shader Delivery Changes It","Shader stutter is a different problem from memory-page prediction and should not be collapsed into one diagnosis.","Separate shader stutter from memory stalls",{},{"id":629,"data":1151,"type":572,"tunes":1153},{"text":1152,"level":47},"Prefetching already exists; the new question is how it predicts",{},{"id":634,"data":1155,"type":544,"tunes":1157},{"text":1156},"Traditional prefetching tries to move data before a processor explicitly asks for it. Hardware prefetchers exploit access patterns. Engines stream world cells before the player reaches them. Operating systems and runtimes keep likely-needed pages hot. The difficult part is false prediction: prefetch too little and you still stall; prefetch too much and you waste bandwidth and evict useful data.",{},{"id":639,"data":1159,"type":659,"tunes":1177},{"steps":1160,"title":1176,"orientation":658},[1161,1164,1167,1170,1173],{"label":1162,"description":1163},"Observe","Collect recent accesses, world state and workload context.",{"label":1165,"description":1166},"Predict","Estimate which pages\u002Fassets\u002Fshaders will be needed next.",{"label":1168,"description":1169},"Move","Transfer or make those resources resident before demand.",{"label":1171,"description":1172},"Use","The GPU consumes the resource without waiting if prediction was correct.",{"label":1174,"description":1175},"Correct","Evict wrong predictions and update the model\u002Fpolicy.","The basic prefetch trade-off",{},{"id":662,"data":1179,"type":572,"tunes":1181},{"text":1180,"level":47},"There is already research evidence for learned GPU prefetching",{},{"id":667,"data":1183,"type":544,"tunes":1185},{"text":1184},"A published study on deep-learning-based prefetching for CPU-GPU Unified Virtual Memory used a Transformer to model global memory-access context and then derived a lighter predictor. Across 11 memory-intensive benchmarks, the paper reported 10.89% performance improvement over the compared framework, a device-memory page hit rate of 89.02% versus 76.10%, and 11.05% lower CPU-GPU interconnect traffic.",{},{"id":672,"data":1187,"type":609,"tunes":1219},{"content":1188,"stretched":705,"withHeadings":15},[1189,1194,1199,1204,1209,1214],[1190,1191,1192,1193],"Research result","Reported value","What it supports","What it does NOT prove",[1195,1196,1197,1198],"Learned UVM performance","+10.89%","Learned access prediction can beat a prior UVM prefetch baseline","A commercial game gains 10.89% FPS",[1200,1201,1202,1203],"UVM page hit rate","89.02% vs 76.10%","Prediction can materially change where requested data is found","Every game texture\u002Fpage is equally predictable",[1205,1206,1207,1208],"CPU–GPU interconnect traffic","−11.05%","Better placement can reduce reactive movement","PCIe traffic always falls in games",[1210,1211,1212,1213],"AAAI attention-kernel efficiency","2.15×","Prefetch can hide expensive memory access behind useful compute","Gaming kernels receive 2.15×",[1215,1216,1217,1218],"AAAI end-to-end throughput","Up to 1.97×","Latency hiding can propagate beyond one kernel","A 1.97× game frame-rate multiplier",{},{"id":708,"data":1221,"type":552,"tunes":1224},{"body":1222,"title":1223,"variant":712},"Those results come from the paper's benchmark set, \u003Cstrong>not from commercial game engines\u003C\u002Fstrong>. They prove that learned memory-access prediction can work in GPU UVM workloads. They do not prove a 10.89% FPS gain or a specific stutter reduction in a game.","Do not copy these numbers onto games",{},{"id":715,"data":1226,"type":544,"tunes":1228},{"text":1227},"The learned-UVM study is useful because it predicts what page access is likely from memory behavior. The 2026 AAAI KV-cache work is useful for a different reason: it shows that a scheduler can exploit otherwise idle bandwidth windows to decide when to move data into a faster cache before demand. Together they support two separate mechanisms in the Figure Rocks hypothesis — prediction and timing — while neither paper is a gaming benchmark.",{},{"id":720,"data":1230,"type":572,"tunes":1232},{"text":1231,"level":47},"Games have extra information a generic prefetcher does not",{},{"id":725,"data":1234,"type":659,"tunes":1258},{"steps":1235,"title":1257,"orientation":658},[1236,1239,1242,1245,1248,1251,1254],{"label":1237,"description":1238},"1. Read deterministic state","Camera direction, position, velocity, level graph, visibility, animation, quest or encounter state.",{"label":1240,"description":1241},"2. Read access history","Which texture pages, meshes, shaders, geometry clusters and model data were recently used.",{"label":1243,"description":1244},"3. Predict the near-future working set","Estimate likely-needed resources several frames or seconds ahead depending on cost.",{"label":1246,"description":1247},"4. Assign confidence and cost","A 95% likely 4 MB asset is a different decision from a 20% likely 2 GB region.",{"label":1249,"description":1250},"5. Prefetch within a bandwidth budget","Do not let prediction itself saturate the path needed by current rendering.",{"label":1252,"description":1253},"6. Measure misses and pollution","Track stalls avoided, wrong predictions and useful data evicted.",{"label":1255,"description":1256},"7. Adapt","Update the predictor or engine policy for the actual scene and player behavior.","The Predictive Gaming Memory Loop",{},{"id":752,"data":1260,"type":552,"tunes":1263},{"body":1261,"title":1262,"variant":756},"A game does not have to choose between hand-authored streaming and AI. \u003Cstrong>The strongest design may be hybrid:\u003C\u002Fstrong> deterministic rules guarantee critical resources, while a learned predictor ranks optional or near-future work, chooses prefetch depth and spends only safe bandwidth headroom. If confidence is low, the system falls back to authored rules.","The interesting hybrid",{},{"id":759,"data":1265,"type":572,"tunes":1267},{"text":1266,"level":47},"The engine can combine known future with predicted future",{},{"id":764,"data":1269,"type":617,"tunes":1322},{"rows":1270,"title":1316,"layout":609,"columns":1317},[1271,1276,1281,1286,1291,1296,1301,1306,1311],{"id":768,"label":1272,"values":1273},"Camera \u002F portal state",{"known":1274,"learned":1275},"Current view, frustum, room graph, visible portal","Which direction the player is likely to turn next",{"id":774,"label":1277,"values":1278},"Player movement",{"known":1279,"learned":1280},"Velocity, input, navigation constraints","Likely traversal path several moments ahead",{"id":780,"label":1282,"values":1283},"Asset dependencies",{"known":1284,"learned":1285},"Dependencies for a known level cell or object","Which optional neighboring assets will become hot",{"id":786,"label":1287,"values":1288},"LOD state",{"known":1289,"learned":1290},"Current distance and thresholds","Which LOD transitions are likely soon",{"id":792,"label":1292,"values":1293},"Material\u002Fshader graph",{"known":1294,"learned":1295},"Shaders required by known visible content","Which variants are likely to be requested next",{"id":798,"label":1297,"values":1298},"Core strength",{"known":1299,"learned":1300},"Knows hard level rules, dependencies and guaranteed transitions","Ranks ambiguous future access from behavior and context",{"id":804,"label":1302,"values":1303},"Failure mode",{"known":1304,"learned":1305},"Can be conservative and load broad sets","Can be wrong and pollute cache or memory",{"id":810,"label":1307,"values":1308},"Best use",{"known":1309,"learned":1310},"Critical assets and guaranteed transitions","Optional prioritization inside a bounded bandwidth budget",{"id":816,"label":1312,"values":1313},"Safety policy",{"known":1314,"learned":1315},"Easy to reason about and test","Needs confidence thresholds, telemetry and deterministic fallback","Deterministic knowledge vs learned prediction",[1318,1320],{"id":824,"label":1319},"Engine already knows",{"id":827,"label":1321},"Predictor can estimate",{},{"id":831,"data":1324,"type":572,"tunes":1326},{"text":1325,"level":47},"A simple example: running toward a door",{},{"id":836,"data":1328,"type":544,"tunes":1330},{"text":1329},"Imagine a player sprinting down a corridor toward a door that opens into a dense market square. A conventional streaming system already knows the next zone and can begin loading it. Predictive memory adds a second layer: based on camera direction, movement, prior player choices and current mission state, it can rank which subset of textures, NPC animation data, shaders and geometry is most likely to be needed first.",{},{"id":841,"data":1332,"type":572,"tunes":1334},{"text":1333,"level":47},"Why this could matter more as neural rendering grows",{},{"id":846,"data":1336,"type":544,"tunes":1338},{"text":1337},"Neural graphics adds new data classes: model weights, latent or compressed representations, history buffers and intermediate tensors. If those resources compete with textures and geometry, residency becomes more complicated. Predictive scheduling could decide not only which asset to move, but which representation to keep: raw data, a compressed neural representation, or a reconstructable form.",{},{"id":851,"data":1340,"type":626,"tunes":1345},{"url":1341,"title":1342,"excerpt":1343,"ctaLabel":1344},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 Is Not Just Upscaling: What Generative Neural Rendering Actually Changes","Neural rendering changes what data a frame needs and when it needs it.","Read the neural rendering analysis",{},{"id":859,"data":1347,"type":572,"tunes":1349},{"text":1348,"level":47},"The failure modes are as important as the opportunity",{},{"id":864,"data":1351,"type":544,"tunes":1353},{"text":1352},"Prefetch is not free. A wrong prediction can fetch resources the player never sees, evict useful pages and compete with frame-critical traffic. The correct goal is therefore not maximum prefetch; it is maximum useful hit rate inside a bounded bandwidth and residency budget.",{},{"id":869,"data":1355,"type":609,"tunes":1384},{"content":1356,"stretched":705,"withHeadings":15},[1357,1360,1364,1368,1372,1376,1380],[1302,1358,1359],"What happens","Mitigation idea",[1361,1362,1363],"Bad prediction","Wrong data is prefetched","Confidence thresholds; reserve budget for deterministic demand",[1365,1366,1367],"Cache pollution","Useful current data is evicted","Separate predicted and protected residency classes",[1369,1370,1371],"Bandwidth contention","Prefetch steals bandwidth from current frame","Schedule only in headroom windows; enforce per-frame budget",[1373,1374,1375],"Behavior shift","Player turns around or fast-travels","Cheap cancellation and rapid fallback",[1377,1378,1379],"Model overhead","Predictor costs more than it saves","Small models, offline training, or rule-derived lightweight predictors",[1381,1382,1383],"Non-memory stutter","Problem remains","Keep diagnosis separate: shader, CPU, network and storage paths need their own tests",{},{"id":901,"data":1386,"type":572,"tunes":1388},{"text":1387,"level":47},"How you would test it in a real engine",{},{"id":906,"data":1390,"type":659,"tunes":1414},{"steps":1391,"title":1413,"orientation":658},[1392,1395,1398,1401,1404,1407,1410],{"label":1393,"description":1394},"A. Establish baseline","Capture frame-time spikes, page faults\u002Fmigrations, residency misses and data-transfer volume without prediction.",{"label":1396,"description":1397},"B. Add deterministic prefetch only","Measure what conventional engine knowledge already solves.",{"label":1399,"description":1400},"C. Add learned ranking","Use the same memory and bandwidth budget so the comparison is fair.",{"label":1402,"description":1403},"D. Measure hit rate and pollution","Track useful prefetched bytes versus wasted bytes and evictions.",{"label":1405,"description":1406},"E. Measure frame-time distribution","Average FPS is not enough; inspect tail latency and hitch frequency.",{"label":1408,"description":1409},"F. Stress behavior changes","Fast turns, teleport, multiplayer unpredictability and procedural scenes.",{"label":1411,"description":1412},"G. Compare cost","Predictor CPU\u002FGPU time + memory overhead must be lower than the stalls it removes.","Predictive Memory Evaluation",{},{"id":933,"data":1416,"type":572,"tunes":1418},{"text":1417,"level":47},"Where this hypothesis stops",{},{"id":938,"data":1420,"type":544,"tunes":1422},{"text":1421},"Predictive memory cannot make a too-small memory pool infinitely large. It cannot create bandwidth. It cannot fix a shader compilation hitch by guessing the next texture. And it becomes less useful when the player's future is intentionally unpredictable or when the engine already has near-perfect deterministic streaming information.",{},{"id":943,"data":1424,"type":572,"tunes":1426},{"text":1425,"level":47},"A light connection to general AI architecture",{},{"id":948,"data":1428,"type":544,"tunes":1430},{"text":1429},"The deeper pattern is similar to retrieval systems outside gaming: the system should not fetch everything, and it should not wait until the final moment to discover that context is missing. It needs a trigger, a candidate set, a ranking policy and a validity boundary. Gaming simply imposes a much tighter latency budget.",{},{"id":953,"data":1432,"type":626,"tunes":1436},{"url":955,"title":1433,"excerpt":1434,"ctaLabel":1435},"What Is RAG? The Simplest Explanation of How It Works","A general explanation of retrieval, context and why systems fetch information instead of placing everything in the model at once.","Read the RAG explainer on stajic.de",{},{"id":961,"data":1438,"type":572,"tunes":1440},{"text":1439,"level":47},"Primary source",{},{"id":966,"data":1442,"type":973,"tunes":1447},{"link":968,"meta":1443},{"image":1444,"title":1445,"description":1446},{"url":13},"Deep learning based data prefetching in CPU-GPU Unified Virtual Memory","Research paper reporting learned UVM page-prefetching results across 11 memory-intensive benchmarks.",{},{"id":976,"data":1449,"type":973,"tunes":1454},{"link":978,"meta":1450},{"image":1451,"title":1452,"description":1453},{"url":13},"AAAI 2026 — Asynchronous KV Cache Prefetching","Published 2026 evidence for proactively using idle memory bandwidth and L2 cache prefetch.",{},{"id":985,"data":1456,"type":1009,"tunes":1474},{"items":1457,"title":1473},[1458,1461,1464,1467,1470],{"id":989,"answer":1459,"question":1460},"Not in the specific Figure Rocks architecture proposed here. Games already use many forms of prefetching and predictive streaming; the hypothesis is to combine deterministic game state with learned GPU-memory prediction.","Is AI prefetching already a standard game-engine feature?",{"id":993,"answer":1462,"question":1463},"No. It aims to improve when and what data is moved so the processor waits less often.","Would this increase RAM or VRAM speed?",{"id":997,"answer":1465,"question":1466},"No. It can only address stalls caused by data availability\u002Fresidency when prediction is accurate enough.","Could it eliminate stutter?",{"id":1001,"answer":1468,"question":1469},"Because engines expose structured information about camera, level topology, assets, animation and likely visibility that a generic application does not.","Why might games be a good prediction target?",{"id":1005,"answer":1471,"question":1472},"Not one metric. Useful evaluation needs page\u002Fresource hit rate, bandwidth overhead, cache pollution and frame-time tail latency.","What metric matters most?","Predictive gaming memory",{},{"id":1012,"data":1476,"type":1030,"tunes":1490},{"title":1477,"entries":1478},"Predictive memory terms",[1479,1482,1485,1487],{"term":1480,"anchor":1018,"definition":1481},"Prefetch","Moving or making data available before explicit demand.",{"term":1483,"anchor":1022,"definition":1484},"Residency","Keeping the resource in memory close enough to the processor that it can be used without an expensive transfer.",{"term":1365,"anchor":1025,"definition":1486},"Replacing useful data with prefetched data that turns out not to be needed.",{"term":1488,"anchor":1028,"definition":1489},"Predictive Gaming Memory","Figure Rocks hypothesis combining deterministic engine state, learned prediction, prefetch and measured residency policy.",{},"2.31.6","Game engines already know where the player, camera and world are going. Research shows learned GPU prefetching can predict memory pages. What happens if those ideas are combined?",{"lang":7,"title":534,"content":536,"contentJson":1494,"excerpt":1033},{"time":538,"blocks":1495,"version":1032},[1496,1499,1502,1505,1508,1511,1528,1531,1534,1537,1546,1549,1552,1562,1565,1568,1571,1582,1585,1588,1613,1616,1619,1622,1625,1628,1631,1634,1645,1648,1659,1662,1665,1668,1671,1674,1677,1682,1687,1696],{"id":541,"data":1497,"type":544,"tunes":1498},{"text":543},{},{"id":547,"data":1500,"type":552,"tunes":1501},{"body":549,"title":550,"variant":551},{},{"id":555,"data":1503,"type":552,"tunes":1504},{"body":557,"title":558,"variant":559},{},{"id":562,"data":1506,"type":566,"tunes":1507},{"title":564,"maxLevel":565,"minLevel":47},{},{"id":569,"data":1509,"type":572,"tunes":1510},{"text":571,"level":47},{},{"id":575,"data":1512,"type":617,"tunes":1527},{"rows":1513,"title":608,"layout":609,"columns":1524},[1514,1516,1518,1520,1522],{"id":579,"label":580,"values":1515},{"other":582,"memory":583},{"id":585,"label":586,"values":1517},{"other":588,"memory":589},{"id":591,"label":592,"values":1519},{"other":594,"memory":595},{"id":597,"label":598,"values":1521},{"other":600,"memory":601},{"id":603,"label":604,"values":1523},{"other":606,"memory":607},[1525,1526],{"id":612,"label":613},{"id":615,"label":616},{},{"id":620,"data":1529,"type":626,"tunes":1530},{"url":622,"title":623,"excerpt":624,"ctaLabel":625},{},{"id":629,"data":1532,"type":572,"tunes":1533},{"text":631,"level":47},{},{"id":634,"data":1535,"type":544,"tunes":1536},{"text":636},{},{"id":639,"data":1538,"type":659,"tunes":1545},{"steps":1539,"title":657,"orientation":658},[1540,1541,1542,1543,1544],{"label":643,"description":644},{"label":646,"description":647},{"label":649,"description":650},{"label":652,"description":653},{"label":655,"description":656},{},{"id":662,"data":1547,"type":572,"tunes":1548},{"text":664,"level":47},{},{"id":667,"data":1550,"type":544,"tunes":1551},{"text":669},{},{"id":672,"data":1553,"type":609,"tunes":1561},{"content":1554,"stretched":705,"withHeadings":15},[1555,1556,1557,1558,1559,1560],[676,677,678,679],[681,682,683,684],[686,687,688,689],[691,692,693,694],[696,697,698,699],[701,702,703,704],{},{"id":708,"data":1563,"type":552,"tunes":1564},{"body":710,"title":711,"variant":712},{},{"id":715,"data":1566,"type":544,"tunes":1567},{"text":717},{},{"id":720,"data":1569,"type":572,"tunes":1570},{"text":722,"level":47},{},{"id":725,"data":1572,"type":659,"tunes":1581},{"steps":1573,"title":749,"orientation":658},[1574,1575,1576,1577,1578,1579,1580],{"label":729,"description":730},{"label":732,"description":733},{"label":735,"description":736},{"label":738,"description":739},{"label":741,"description":742},{"label":744,"description":745},{"label":747,"description":748},{},{"id":752,"data":1583,"type":552,"tunes":1584},{"body":754,"title":755,"variant":756},{},{"id":759,"data":1586,"type":572,"tunes":1587},{"text":761,"level":47},{},{"id":764,"data":1589,"type":617,"tunes":1612},{"rows":1590,"title":821,"layout":609,"columns":1609},[1591,1593,1595,1597,1599,1601,1603,1605,1607],{"id":768,"label":769,"values":1592},{"known":771,"learned":772},{"id":774,"label":775,"values":1594},{"known":777,"learned":778},{"id":780,"label":781,"values":1596},{"known":783,"learned":784},{"id":786,"label":787,"values":1598},{"known":789,"learned":790},{"id":792,"label":793,"values":1600},{"known":795,"learned":796},{"id":798,"label":799,"values":1602},{"known":801,"learned":802},{"id":804,"label":805,"values":1604},{"known":807,"learned":808},{"id":810,"label":811,"values":1606},{"known":813,"learned":814},{"id":816,"label":817,"values":1608},{"known":819,"learned":820},[1610,1611],{"id":824,"label":825},{"id":827,"label":828},{},{"id":831,"data":1614,"type":572,"tunes":1615},{"text":833,"level":47},{},{"id":836,"data":1617,"type":544,"tunes":1618},{"text":838},{},{"id":841,"data":1620,"type":572,"tunes":1621},{"text":843,"level":47},{},{"id":846,"data":1623,"type":544,"tunes":1624},{"text":848},{},{"id":851,"data":1626,"type":626,"tunes":1627},{"url":853,"title":854,"excerpt":855,"ctaLabel":856},{},{"id":859,"data":1629,"type":572,"tunes":1630},{"text":861,"level":47},{},{"id":864,"data":1632,"type":544,"tunes":1633},{"text":866},{},{"id":869,"data":1635,"type":609,"tunes":1644},{"content":1636,"stretched":705,"withHeadings":15},[1637,1638,1639,1640,1641,1642,1643],[805,873,874],[876,877,878],[880,881,882],[884,885,886],[888,889,890],[892,893,894],[896,897,898],{},{"id":901,"data":1646,"type":572,"tunes":1647},{"text":903,"level":47},{},{"id":906,"data":1649,"type":659,"tunes":1658},{"steps":1650,"title":930,"orientation":658},[1651,1652,1653,1654,1655,1656,1657],{"label":910,"description":911},{"label":913,"description":914},{"label":916,"description":917},{"label":919,"description":920},{"label":922,"description":923},{"label":925,"description":926},{"label":928,"description":929},{},{"id":933,"data":1660,"type":572,"tunes":1661},{"text":935,"level":47},{},{"id":938,"data":1663,"type":544,"tunes":1664},{"text":940},{},{"id":943,"data":1666,"type":572,"tunes":1667},{"text":945,"level":47},{},{"id":948,"data":1669,"type":544,"tunes":1670},{"text":950},{},{"id":953,"data":1672,"type":626,"tunes":1673},{"url":955,"title":956,"excerpt":957,"ctaLabel":958},{},{"id":961,"data":1675,"type":572,"tunes":1676},{"text":963,"level":47},{},{"id":966,"data":1678,"type":973,"tunes":1681},{"link":968,"meta":1679},{"image":1680,"title":971,"description":972},{"url":13},{},{"id":976,"data":1683,"type":973,"tunes":1686},{"link":978,"meta":1684},{"image":1685,"title":981,"description":982},{"url":13},{},{"id":985,"data":1688,"type":1009,"tunes":1695},{"items":1689,"title":1008},[1690,1691,1692,1693,1694],{"id":989,"answer":990,"question":991},{"id":993,"answer":994,"question":995},{"id":997,"answer":998,"question":999},{"id":1001,"answer":1002,"question":1003},{"id":1005,"answer":1006,"question":1007},{},{"id":1012,"data":1697,"type":1030,"tunes":1703},{"title":1014,"entries":1698},[1699,1700,1701,1702],{"term":1017,"anchor":1018,"definition":1019},{"term":1021,"anchor":1022,"definition":1023},{"term":880,"anchor":1025,"definition":1026},{"term":1008,"anchor":1028,"definition":1029},{},"Post erfolgreich abgerufen",{"items":1706,"source":1773,"manualIds":1774,"manualMatchedIds":1775},[1707,1713,1718,1723,1728,1734,1739,1745,1750,1756,1761,1766],{"id":1708,"slug":1709,"title":1710,"excerpt":1711,"featuredImage":14,"publishedAt":1712},"48","gaming-baseline-setup-frame-pacing-vrr-and-latency-pc-and-console","Configuration de base pour le gaming : Frame Pacing, VRR et latence (PC + Console)","Une base pratique qui améliore tout le ressenti avant d'acheter quoi que ce soit : un frame pacing stable, un VRR correct et une latence réduite pour PC et consoles.","2026-02-19T11:00:00.000Z",{"id":1714,"slug":1715,"title":1716,"excerpt":1717,"featuredImage":14,"publishedAt":1712},"17","fix-packet-loss-the-hidden-cause-of-random-online-problems","Corriger la perte de paquets : la cause cachée des problèmes en ligne ‘aléatoires’","La perte de paquets se traduit par un lag aléatoire, des coups retardés et des mouvements saccadés. Ce guide permet d'isoler l'endroit où la perte se produit et de savoir quoi corriger en premier.",{"id":1719,"slug":1720,"title":1721,"excerpt":1722,"featuredImage":14,"publishedAt":1712},"62","oled-vs-ips-vs-va-for-gaming-what-you-gain-and-what-you-lose","OLED vs IPS vs VA pour le jeu : ce que vous gagnez et ce que vous perdez","Le type de dalle est une affaire de compromis : mouvement, contraste, angles de vision et comportement de la latence. Apprenez ce qui compte pour le ressenti, pas le marketing.",{"id":1724,"slug":1725,"title":1726,"excerpt":1727,"featuredImage":14,"publishedAt":1712},"42","the-feel-trinity-clarity-responsiveness-comfort","La Trinité du Ressenti : Clarté, Réactivité, Confort","Le modèle fondamental derrière figure.rocks. Trois résultats qui déterminent si l'expérience de jeu semble premium ou misérable — quelle que soit la plateforme.",{"id":1729,"slug":1730,"title":1731,"excerpt":14,"featuredImage":1732,"publishedAt":1733},"456","what-would-a-spark-like-gaming-pc-actually-change-unified-memory-ai-compute-and-the-classic-gpu-split","Que changerait réellement un PC de jeu de type Spark ? Mémoire unifiée, calcul IA et la classique séparation GPU","\u002Fuploads\u002F2026\u002F10\u002Fwhat-would-a-spark-like-gaming-pc-actually-change-unified-memory-ai-compute-and-the-classic-gpu-split-1792340021752-bia7n6.webp","2026-10-18T12:12:00.000Z",{"id":283,"slug":1735,"title":1736,"excerpt":1737,"featuredImage":14,"publishedAt":1738},"responsiveness-in-gaming-what-it-really-means","Réactivité dans le jeu vidéo : ce que cela signifie réellement","La réactivité est la sensation d'un contrôle instantané. Voici ce qui la crée, ce qui la détruit, et comment l'améliorer sans courir après les mythes.","2026-02-19T12:00:00.000Z",{"id":1740,"slug":1741,"title":1742,"excerpt":1743,"featuredImage":14,"publishedAt":1744},"402","ll-amiibo-compatible-games-complete-overview-of-bonuses-and-gameplay-integration","Tous les jeux compatibles avec les amiibo – Aperçu complet des bonus et de l'intégration au gameplay","Les figurines amiibo interagissent avec une large gamme de jeux Nintendo sur Wii U, Nintendo 3DS et Nintendo Switch. La valeur ajoutée diffère d'un titre à l'autre. Dans certains cas, le bonus est cosmétique. Dans d'autres, il affecte le gameplay plus directement. Ce qui suit est un aperçu structuré des titres compatibles et du déblocage le plus significatif lié au scan d'une figurine.","2026-02-27T18:13:00.000Z",{"id":1746,"slug":1747,"title":1748,"excerpt":1749,"featuredImage":14,"publishedAt":1712},"60","gaming-audio-positioning-clarity-beats-bass-headset-setup-that-works","Positionnement audio en jeu : la clarté l'emporte sur les basses (une configuration de casque qui fonctionne)","L'audio compétitif est une question d'information : bruits de pas, direction, distance et timing. Apprenez une base pratique pour votre casque et les erreurs qui nuisent au positionnement.",{"id":1751,"slug":1752,"title":1753,"excerpt":1754,"featuredImage":14,"publishedAt":1755},"245","qos-myths-for-gaming-what-helps-what-hurts-and-the-real-priority","Mythes sur la QoS pour le gaming : ce qui aide, ce qui nuit et la véritable priorité","La QoS n'est pas un bouton magique pour le ‘gaming’. Une bonne gestion des files d'attente réduit la latence en charge. Une mauvaise QoS ajoute de la gigue ou rompt l'équité. Voici la manière stable d'envisager la chose.","2026-02-20T23:55:00.000Z",{"id":1757,"slug":1758,"title":1759,"excerpt":1760,"featuredImage":14,"publishedAt":1712},"66","experience-first-why-gaming-feel-beats-specs-a-value-standard","L'expérience d'abord : Pourquoi le ressenti de jeu surpasse les caractéristiques (Un standard de valeur)","Internet vend des chiffres. Les joueurs ressentent le timing. C'est un standard de valeur pratique : l'expérience d'abord, la stabilité d'abord, puis le matériel et le prix.",{"id":1762,"slug":1763,"title":1764,"excerpt":1765,"featuredImage":14,"publishedAt":1712},"37","the-gaming-feel-chain-why-one-upgrade-often-does-nothing","La chaîne du ressenti en jeu : pourquoi une seule amélioration ne change souvent rien","Le ressenti est une chaîne : input, système, frame pacing, synchronisation, affichage, réseau. Si un maillon est instable, les améliorations ne se font pas sentir.",{"id":1767,"slug":1768,"title":1769,"excerpt":1770,"featuredImage":1771,"publishedAt":1772},"457","what-does-future-proof-gaming-hardware-mean-in-the-neural-rendering-era","Que signifie « matériel de jeu à l'épreuve du temps » à l'ère du rendu neuronal ?","La pérennisation consistait autrefois à acheter plus de CPU, de GPU et de VRAM que nécessaire aujourd'hui. Le rendu neuronal rend cette règle moins fiable. Voici un meilleur cadre de décision.","\u002Fuploads\u002F2026\u002F10\u002Fwhat-does-future-proof-gaming-hardware-mean-in-the-neural-rendering-era-1792340303825-012uhb.webp","2026-10-18T12:16:00.000Z","fallback",[],[]]