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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":537,"blocks":538,"version":1032},1792339102894,[539,545,553,560,567,573,618,627,632,637,660,665,670,706,713,718,723,750,757,762,829,834,839,844,849,857,862,867,899,904,931,936,941,946,951,959,964,974,983,1010],{"id":540,"data":541,"type":543,"tunes":544},"p-0075",{"text":542},"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.","paragraph",{},{"id":546,"data":547,"type":551,"tunes":552},"c-0076",{"body":548,"title":549,"variant":550},"\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","info","callout",{},{"id":554,"data":555,"type":551,"tunes":559},"c-0077",{"body":556,"title":557,"variant":558},"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","note",{},{"id":561,"data":562,"type":565,"tunes":566},"toc-0078",{"title":563,"maxLevel":564,"minLevel":46},"Contents",3,"tableOfContents",{},{"id":568,"data":569,"type":571,"tunes":572},"h-0079",{"text":570,"level":46},"First separate memory stalls from other stutter","header",{},{"id":574,"data":575,"type":616,"tunes":617},"cmp-0080",{"rows":576,"title":607,"layout":608,"columns":609},[577,583,589,595,601],{"id":578,"label":579,"values":580},"asset","Asset missing from fast memory",{"other":581,"memory":582},"Storage or decompression can also be involved","Possible",{"id":584,"label":585,"values":586},"shader","Shader not ready",{"other":587,"memory":588},"Compilation\u002Fpipeline creation can be the real cause","May involve caches",{"id":590,"label":591,"values":592},"cpu","CPU simulation spike",{"other":593,"memory":594},"Game logic or draw-call work may dominate","Cache\u002Fmemory behavior can contribute",{"id":596,"label":597,"values":598},"network","Online hitch",{"other":599,"memory":600},"Server or packet timing can dominate","Usually not the primary cause",{"id":602,"label":603,"values":604},"vram","Eviction\u002Fresidency pressure",{"other":605,"memory":606},"Can be misdiagnosed from allocation meters alone","Directly relevant","Stutter sources that can look similar","table",[610,613],{"id":611,"label":612},"memory","Memory\u002Fdata path",{"id":614,"label":615},"other","Other path","comparison",{},{"id":619,"data":620,"type":625,"tunes":626},"ref-0081",{"url":621,"title":622,"excerpt":623,"ctaLabel":624},"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","referralArticle",{},{"id":628,"data":629,"type":571,"tunes":631},"h-0082",{"text":630,"level":46},"Prefetching already exists; the new question is how it predicts",{},{"id":633,"data":634,"type":543,"tunes":636},"p-0083",{"text":635},"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":638,"data":639,"type":658,"tunes":659},"flow-0084",{"steps":640,"title":656,"orientation":657},[641,644,647,650,653],{"label":642,"description":643},"Observe","Collect recent accesses, world state and workload context.",{"label":645,"description":646},"Predict","Estimate which pages\u002Fassets\u002Fshaders will be needed next.",{"label":648,"description":649},"Move","Transfer or make those resources resident before demand.",{"label":651,"description":652},"Use","The GPU consumes the resource without waiting if prediction was correct.",{"label":654,"description":655},"Correct","Evict wrong predictions and update the model\u002Fpolicy.","The basic prefetch trade-off","auto","processFlow",{},{"id":661,"data":662,"type":571,"tunes":664},"h-0085",{"text":663,"level":46},"There is already research evidence for learned GPU prefetching",{},{"id":666,"data":667,"type":543,"tunes":669},"p-0086",{"text":668},"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":671,"data":672,"type":608,"tunes":705},"t-0087",{"content":673,"stretched":704,"withHeadings":14},[674,679,684,689,694,699],[675,676,677,678],"Research result","Reported value","What it supports","What it does NOT prove",[680,681,682,683],"Learned UVM performance","+10.89%","Learned access prediction can beat a prior UVM prefetch baseline","A commercial game gains 10.89% FPS",[685,686,687,688],"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",[690,691,692,693],"CPU–GPU interconnect traffic","−11.05%","Better placement can reduce reactive movement","PCIe traffic always falls in games",[695,696,697,698],"AAAI attention-kernel efficiency","2.15×","Prefetch can hide expensive memory access behind useful compute","Gaming kernels receive 2.15×",[700,701,702,703],"AAAI end-to-end throughput","Up to 1.97×","Latency hiding can propagate beyond one kernel","A 1.97× game frame-rate multiplier",false,{},{"id":707,"data":708,"type":551,"tunes":712},"c-0088",{"body":709,"title":710,"variant":711},"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","warning",{},{"id":714,"data":715,"type":543,"tunes":717},"v3-two-evidence-p",{"text":716},"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":719,"data":720,"type":571,"tunes":722},"h-0089",{"text":721,"level":46},"Games have extra information a generic prefetcher does not",{},{"id":724,"data":725,"type":658,"tunes":749},"flow-0090",{"steps":726,"title":748,"orientation":657},[727,730,733,736,739,742,745],{"label":728,"description":729},"1. Read deterministic state","Camera direction, position, velocity, level graph, visibility, animation, quest or encounter state.",{"label":731,"description":732},"2. Read access history","Which texture pages, meshes, shaders, geometry clusters and model data were recently used.",{"label":734,"description":735},"3. Predict the near-future working set","Estimate likely-needed resources several frames or seconds ahead depending on cost.",{"label":737,"description":738},"4. Assign confidence and cost","A 95% likely 4 MB asset is a different decision from a 20% likely 2 GB region.",{"label":740,"description":741},"5. Prefetch within a bandwidth budget","Do not let prediction itself saturate the path needed by current rendering.",{"label":743,"description":744},"6. Measure misses and pollution","Track stalls avoided, wrong predictions and useful data evicted.",{"label":746,"description":747},"7. Adapt","Update the predictor or engine policy for the actual scene and player behavior.","The Predictive Gaming Memory Loop",{},{"id":751,"data":752,"type":551,"tunes":756},"c-0091",{"body":753,"title":754,"variant":755},"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","success",{},{"id":758,"data":759,"type":571,"tunes":761},"v3-known-future",{"text":760,"level":46},"The engine can combine known future with predicted future",{},{"id":763,"data":764,"type":616,"tunes":828},"v3-known-pred",{"rows":765,"title":820,"layout":608,"columns":821},[766,772,778,784,790,796,802,808,814],{"id":767,"label":768,"values":769},"camera","Camera \u002F portal state",{"known":770,"learned":771},"Current view, frustum, room graph, visible portal","Which direction the player is likely to turn next",{"id":773,"label":774,"values":775},"movement","Player movement",{"known":776,"learned":777},"Velocity, input, navigation constraints","Likely traversal path several moments ahead",{"id":779,"label":780,"values":781},"assets","Asset dependencies",{"known":782,"learned":783},"Dependencies for a known level cell or object","Which optional neighboring assets will become hot",{"id":785,"label":786,"values":787},"lod","LOD state",{"known":788,"learned":789},"Current distance and thresholds","Which LOD transitions are likely soon",{"id":791,"label":792,"values":793},"shaders","Material\u002Fshader graph",{"known":794,"learned":795},"Shaders required by known visible content","Which variants are likely to be requested next",{"id":797,"label":798,"values":799},"strength","Core strength",{"known":800,"learned":801},"Knows hard level rules, dependencies and guaranteed transitions","Ranks ambiguous future access from behavior and context",{"id":803,"label":804,"values":805},"risk","Failure mode",{"known":806,"learned":807},"Can be conservative and load broad sets","Can be wrong and pollute cache or memory",{"id":809,"label":810,"values":811},"best","Best use",{"known":812,"learned":813},"Critical assets and guaranteed transitions","Optional prioritization inside a bounded bandwidth budget",{"id":815,"label":816,"values":817},"safety","Safety policy",{"known":818,"learned":819},"Easy to reason about and test","Needs confidence thresholds, telemetry and deterministic fallback","Deterministic knowledge vs learned prediction",[822,825],{"id":823,"label":824},"known","Engine already knows",{"id":826,"label":827},"learned","Predictor can estimate",{},{"id":830,"data":831,"type":571,"tunes":833},"h-0092",{"text":832,"level":46},"A simple example: running toward a door",{},{"id":835,"data":836,"type":543,"tunes":838},"p-0093",{"text":837},"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":840,"data":841,"type":571,"tunes":843},"h-0095",{"text":842,"level":46},"Why this could matter more as neural rendering grows",{},{"id":845,"data":846,"type":543,"tunes":848},"p-0096",{"text":847},"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":850,"data":851,"type":625,"tunes":856},"ref-0097",{"url":852,"title":853,"excerpt":854,"ctaLabel":855},"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":858,"data":859,"type":571,"tunes":861},"h-0098",{"text":860,"level":46},"The failure modes are as important as the opportunity",{},{"id":863,"data":864,"type":543,"tunes":866},"v3-bandwidth-budget-p",{"text":865},"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":868,"data":869,"type":608,"tunes":898},"t-0099",{"content":870,"stretched":704,"withHeadings":14},[871,874,878,882,886,890,894],[804,872,873],"What happens","Mitigation idea",[875,876,877],"Bad prediction","Wrong data is prefetched","Confidence thresholds; reserve budget for deterministic demand",[879,880,881],"Cache pollution","Useful current data is evicted","Separate predicted and protected residency classes",[883,884,885],"Bandwidth contention","Prefetch steals bandwidth from current frame","Schedule only in headroom windows; enforce per-frame budget",[887,888,889],"Behavior shift","Player turns around or fast-travels","Cheap cancellation and rapid fallback",[891,892,893],"Model overhead","Predictor costs more than it saves","Small models, offline training, or rule-derived lightweight predictors",[895,896,897],"Non-memory stutter","Problem remains","Keep diagnosis separate: shader, CPU, network and storage paths need their own tests",{},{"id":900,"data":901,"type":571,"tunes":903},"h-0100",{"text":902,"level":46},"How you would test it in a real engine",{},{"id":905,"data":906,"type":658,"tunes":930},"flow-0101",{"steps":907,"title":929,"orientation":657},[908,911,914,917,920,923,926],{"label":909,"description":910},"A. Establish baseline","Capture frame-time spikes, page faults\u002Fmigrations, residency misses and data-transfer volume without prediction.",{"label":912,"description":913},"B. Add deterministic prefetch only","Measure what conventional engine knowledge already solves.",{"label":915,"description":916},"C. Add learned ranking","Use the same memory and bandwidth budget so the comparison is fair.",{"label":918,"description":919},"D. Measure hit rate and pollution","Track useful prefetched bytes versus wasted bytes and evictions.",{"label":921,"description":922},"E. Measure frame-time distribution","Average FPS is not enough; inspect tail latency and hitch frequency.",{"label":924,"description":925},"F. Stress behavior changes","Fast turns, teleport, multiplayer unpredictability and procedural scenes.",{"label":927,"description":928},"G. Compare cost","Predictor CPU\u002FGPU time + memory overhead must be lower than the stalls it removes.","Predictive Memory Evaluation",{},{"id":932,"data":933,"type":571,"tunes":935},"h-0102",{"text":934,"level":46},"Where this hypothesis stops",{},{"id":937,"data":938,"type":543,"tunes":940},"p-0103",{"text":939},"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":942,"data":943,"type":571,"tunes":945},"h-0104",{"text":944,"level":46},"A light connection to general AI architecture",{},{"id":947,"data":948,"type":543,"tunes":950},"p-0105",{"text":949},"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":952,"data":953,"type":625,"tunes":958},"ref-0106",{"url":954,"title":955,"excerpt":956,"ctaLabel":957},"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works","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":960,"data":961,"type":571,"tunes":963},"h-0107",{"text":962,"level":46},"Primary source",{},{"id":965,"data":966,"type":972,"tunes":973},"src-0108",{"link":967,"meta":968},"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490",{"image":969,"title":970,"description":971},{"url":12},"Deep learning based data prefetching in CPU-GPU Unified Virtual Memory","Research paper reporting learned UVM page-prefetching results across 11 memory-intensive benchmarks.","linkTool",{},{"id":975,"data":976,"type":972,"tunes":982},"v3-src-aaai2",{"link":977,"meta":978},"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224",{"image":979,"title":980,"description":981},{"url":12},"AAAI 2026 — Asynchronous KV Cache Prefetching","Published 2026 evidence for proactively using idle memory bandwidth and L2 cache prefetch.",{},{"id":984,"data":985,"type":1008,"tunes":1009},"faq-0109",{"items":986,"title":1007},[987,991,995,999,1003],{"id":988,"answer":989,"question":990},"faq1","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":992,"answer":993,"question":994},"faq2","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":996,"answer":997,"question":998},"faq3","No. It can only address stalls caused by data availability\u002Fresidency when prediction is accurate enough.","Could it eliminate stutter?",{"id":1000,"answer":1001,"question":1002},"faq4","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":1004,"answer":1005,"question":1006},"faq5","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","faq",{},{"id":1011,"data":1012,"type":1030,"tunes":1031},"gloss-0110",{"title":1013,"entries":1014},"Predictive memory terms",[1015,1019,1023,1026],{"term":1016,"anchor":1017,"definition":1018},"Prefetch","prefetch","Moving or making data available before explicit demand.",{"term":1020,"anchor":1021,"definition":1022},"Residency","residency","Keeping the resource in memory close enough to the processor that it can be used without an expensive transfer.",{"term":879,"anchor":1024,"definition":1025},"cache-pollution","Replacing useful data with prefetched data that turns out not to be needed.",{"term":1027,"anchor":1028,"definition":1029},"Predictive Gaming Memory","predictive-gaming-memory","Figure Rocks hypothesis combining deterministic engine state, learned prediction, prefetch and measured residency policy.","glossary",{},"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 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It connects collector demand, franchise identity, and the wider appeal of Doom as one of gaming’s most durable icons.","\u002Fuploads\u002F2026\u002F05\u002Fpre-order-alert-good-smile-company-figma-doom-the-dark-ages-doom-slayer-dx-edition-1779227794098-251pwj.webp","2026-04-22T04:37:00.000Z",{"id":1315,"slug":1316,"title":1317,"excerpt":1318,"featuredImage":13,"publishedAt":1319},"198","mtu-myths-for-gaming-when-changing-mtu-helps-rare-vs-breaks-things","MTU Myths for Gaming: When Changing MTU Helps (Rare) vs Breaks Things","MTU tweaks are usually placebo and can cause fragmentation issues. Learn when MTU changes are actually relevant and a safe approach if you must test.","2026-02-20T18:00:00.000Z",{"id":1321,"slug":1322,"title":1323,"excerpt":1324,"featuredImage":13,"publishedAt":1300},"35","products-objects-that-improve-gaming-feel","Products: Objects That Improve Gaming Feel","Curated products for better play: clarity, responsiveness, comfort, and stability — chosen by experience, not by hype.",{"id":1326,"slug":1327,"title":1328,"excerpt":1329,"featuredImage":1330,"publishedAt":1331},"454","why-ai-is-reshaping-gaming-memory-hbm-gddr-vram-and-the-fight-for-capacity"," Why AI Is Reshaping Gaming Memory: HBM, GDDR, VRAM and the Fight for Capacity","The memory problem behind modern gaming is no longer only about how many gigabytes a GPU needs. AI infrastructure is changing which memory products suppliers prioritize and how much consumer buyers can afford.","\u002Fuploads\u002F2026\u002F10\u002Fwhy-ai-is-reshaping-gaming-memory-hbm-gddr-vram-and-the-fight-for-capacity-1792335495408-ymehaw.webp","2026-10-18T10:56:00.000Z",{"id":1333,"slug":1334,"title":1335,"excerpt":1336,"featuredImage":13,"publishedAt":1337},"398","upcoming-nintendo-amiibo-new-figures-release-windows-and-functional-significance","Upcoming Nintendo amiibo – New Figures, Release Windows and Functional Significance","amiibo are physical figures equipped with NFC chips that communicate with Nintendo consoles. They unlock in-game items, cosmetic elements, or small gameplay functions in compatible titles. This catalog overview focuses on officially announced upcoming amiibo, their expected release windows, their functional value, and their position within the broader wave structure.","2026-02-27T15:54:00.000Z",{"id":1339,"slug":1340,"title":1341,"excerpt":1342,"featuredImage":13,"publishedAt":1300},"21","gaming-controls-build-input-feel-without-hand-pain","Gaming Controls: Build Input Feel Without Hand Pain","Controls shape responsiveness and comfort. This guide focuses on ergonomics, consistency, and practical setup habits.",{"id":1344,"slug":1345,"title":1346,"excerpt":1347,"featuredImage":13,"publishedAt":1348},"399","beyond-scanning-creative-uses-and-modifications-of-amiibo","Beyond Scanning – Creative Uses and Modifications of amiibo\"","amiibo are NFC-equipped character figures released by Nintendo since 2014. Technically, they are small data carriers inside molded plastic figures. In practice, they sit somewhere between toy, collectible, and interface device. Scanning them into a console is only one part of their life cycle. Observed over the years, many owners treat them as objects with their own creative potential.","2026-02-27T16:36:00.000Z",{"id":1350,"slug":1351,"title":1352,"excerpt":1353,"featuredImage":13,"publishedAt":1354},"36","how-we-test-gear-a-simple-experience-first-method","How We Test Gear: A Simple Experience-First Method","A clear method: define the outcome, stabilize the chain, test one change, and keep only what improves real play.","2026-02-19T12:00:00.000Z",{"id":1356,"slug":1357,"title":1358,"excerpt":1359,"featuredImage":13,"publishedAt":1300},"48","gaming-baseline-setup-frame-pacing-vrr-and-latency-pc-and-console","Gaming Baseline Setup: Frame Pacing, VRR, and Latency (PC + Console)","A practical baseline that makes everything feel better before you buy anything: stable frame pacing, correct VRR, and reduced latency for PC and consoles.",{"id":1361,"slug":1362,"title":1363,"excerpt":1364,"featuredImage":13,"publishedAt":1365},"295","amiibo-hub-start-here-pillars-guides-and-what-to-buy-first","amiibo Hub: Start Here (Pillars, Guides, and What to Buy First)","Your Amiibo library in one place: basics, compatibility, buying, and collecting. Use this hub to choose your path and avoid beginner mistakes.","2026-02-21T15:00:00.000Z","fallback",[],[]]