[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-turboquant-wont-fix-memory-crunch-en":3,"tags-turboquant-wont-fix-memory-crunch-en":30,"related-lang-turboquant-wont-fix-memory-crunch-en":41,"related-posts-turboquant-wont-fix-memory-crunch-en":45,"series-research-d4867ede-353b-4812-aac7-aebe28ef3613":82},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":18,"translated_content":10,"views":19,"is_premium":20,"created_at":21,"updated_at":21,"cover_image":11,"published_at":22,"rewrite_status":23,"rewrite_error":10,"rewritten_from_id":24,"slug":25,"category":26,"related_article_id":27,"status":28,"google_indexed_at":29,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":10,"topic_cluster_id":10,"embedding":10,"is_canonical_seed":20},"d4867ede-353b-4812-aac7-aebe28ef3613","TurboQuant Won’t Fix the Memory Crunch","\u003Cp>Google says \u003Ca href=\"https:\u002F\u002Fai.google.dev\u002Fblog\u002Fturboquant\" target=\"_blank\" rel=\"noopener\">TurboQuant\u003C\u002Fa> can cut KV-cache memory use by as much as 6x, and that number is exactly why the AI hardware crowd reacted so fast. The catch is simple: if models get cheaper to run, teams usually ask for longer context windows, more agents, and bigger batches.\u003C\u002Fp>\u003Cp>That matters because memory prices are already under pressure. Inference stacks that once treated KV caches as a secondary cost are now bumping into them as a major bill, especially when chat sessions stretch into hundreds of thousands of tokens.\u003C\u002Fp>\u003Ch2>What TurboQuant actually changes\u003C\u002Fh2>\u003Cp>TurboQuant is a quantization method for KV caches, the short-term memory that helps a model keep track of a conversation during inference. Instead of shrinking the model weights themselves, it compresses the cached key and value vectors that accumulate as prompts grow.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775132152400-1kew.png\" alt=\"TurboQuant Won’t Fix the Memory Crunch\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>That is a useful distinction. A lot of the public talk around quantization focuses on model weights, but KV-cache storage can quickly overtake the model in memory use when context windows get large. Google’s pitch is that TurboQuant brings that cache down to a much smaller footprint without wrecking output quality.\u003C\u002Fp>\u003Cp>The company says the method can reach quality close to BF16 while using just 3.5 bits, and it claims up to an 8x speedup at 4-bit precision for the attention-logit step on \u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fh100\u002F\" target=\"_blank\" rel=\"noopener\">NVIDIA H100\u003C\u002Fa> GPUs. That is a serious claim, because attention is one of the hottest parts of inference.\u003C\u002Fp>\u003Cul>\u003Cli>Google claims up to 6x lower KV-cache memory use\u003C\u002Fli>\u003Cli>Google claims up to 8x speedup for attention logits at 4-bit precision on H100s\u003C\u002Fli>\u003Cli>TurboQuant targets KV caches, not model weights\u003C\u002Fli>\u003Cli>The method combines Quantized Johnson-Lindenstrauss and PolarQuant\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Google also says it tested KV caches down to 2.5 bits with minimal quality loss. If that holds up outside the lab, it gives inference teams a new way to trade memory for throughput.\u003C\u002Fp>\u003Ch2>How PolarQuant and QJL do the heavy lifting\u003C\u002Fh2>\u003Cp>TurboQuant mixes two ideas: PolarQuant and Quantized Johnson-Lindenstrauss, often shortened to QJL. PolarQuant maps the cache vectors onto a circular grid using polar coordinates, which changes how the data is represented before it is compressed.\u003C\u002Fp>\u003Cp>In plain English, Google is trying to store the same information with less bookkeeping. The company says this cuts the overhead that usually comes from normalization and other steps that quantization methods need to stay accurate.\u003C\u002Fp>\u003Cblockquote>“This is comparable to replacing ‘Go 3 blocks east, 4 blocks north’ with ‘go 5 blocks total at a 37-degree angle,’” Google’s blog post says.\u003C\u002Fblockquote>\u003Cp>QJL then corrects errors introduced by the first stage and helps preserve the attention score the model uses to decide what matters in the prompt. That combination is the real trick: less memory use without forcing inference quality to collapse.\u003C\u002Fp>\u003Cp>This is why the idea matters beyond one model family. If the method works across workloads, it could reduce the cost of serving long chats, code assistants, and \u003Ca href=\"\u002Fnews\u002Fai-agent-workflows-context-actions-verification-en\">agent workflows\u003C\u002Fa> that keep large amounts of context alive for long periods.\u003C\u002Fp>\u003Ch2>Why cheaper inference may still mean more memory demand\u003C\u002Fh2>\u003Cp>The obvious reaction is to assume lower memory use will cool demand for DRAM and NAND. That sounds neat, but it misses how AI teams behave when a bottleneck gets cheaper. They usually spend the savings on bigger prompts, longer sessions, and more concurrent users.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775132151260-09u7.png\" alt=\"TurboQuant Won’t Fix the Memory Crunch\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>That pattern is already visible in model development. A year ago, open-weight models often shipped with context windows of 64,000 to 256,000 tokens. Today, one-million-token contexts are no longer rare in open models, and code tools are pushing those limits even harder.\u003C\u002Fp>\u003Cp>For inference providers, TurboQuant creates two options: run the same model with less memory, or use the freed-up capacity to serve longer contexts. In practice, longer contexts look more attractive, because they unlock better code completion, deeper document analysis, and more capable agent loops.\u003C\u002Fp>\u003Cul>\u003Cli>Open models moved from 64,000-256,000 token contexts to 1,000,000+ token contexts in a year\u003C\u002Fli>\u003Cli>TrendForce said TurboQuant may increase demand for long-context applications\u003C\u002Fli>\u003Cli>Longer contexts raise memory needs even when cache compression improves\u003C\u002Fli>\u003Cli>Inference providers can absorb savings by serving more tokens per request\u003C\u002Fli>\u003C\u002Ful>\u003Cp>That is why the memory market reaction looks overdone. Google may lower the memory cost per token, but the industry keeps raising the number of tokens per session. Those forces pull in opposite directions, and the second one has been winning.\u003C\u002Fp>\u003Ch2>What this means for DRAM, NAND, and AI builders\u003C\u002Fh2>\u003Cp>For memory vendors, TurboQuant is less a threat than a sign that AI demand is still expanding into new corners of the stack. If KV caches become cheaper, the natural next step is to build products that keep more context alive, not to stop buying memory.\u003C\u002Fp>\u003Cp>That matters for \u003Ca href=\"https:\u002F\u002Fwww.skhynix.com\u002Feng\u002Fmain\u002F\" target=\"_blank\" rel=\"noopener\">SK hynix\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fwww.samsung.com\u002Fsemiconductor\u002F\" target=\"_blank\" rel=\"noopener\">Samsung Semiconductor\u003C\u002Fa>, and \u003Ca href=\"https:\u002F\u002Fwww.micron.com\u002F\" target=\"_blank\" rel=\"noopener\">Micron\u003C\u002Fa>, because AI workloads are shaping demand for high-bandwidth memory, DRAM, and NAND storage at the same time. The mix changes, but the appetite stays high.\u003C\u002Fp>\u003Cp>For builders, the more interesting question is where to spend the savings. If you are running a coding agent or retrieval-heavy assistant, the answer is probably context. If you are serving a consumer chatbot at scale, the answer may be throughput and lower latency. Either way, the pressure shifts, it does not disappear.\u003C\u002Fp>\u003Cp>Google is also hinting at another use case: vector databases. That matters for search, retrieval, and agent memory systems, where embedding storage and similarity search already eat a lot of infrastructure budget. If TurboQuant-like methods spread there too, the winners will be the teams that can turn lower storage cost into better product quality.\u003C\u002Fp>\u003Cp>My read is simple: TurboQuant is a real efficiency win, but it is also a demand amplifier. The companies that benefit most will be the ones that use the savings to ship longer-context products before their competitors do.\u003C\u002Fp>\u003Ch2>The real test is what teams build next\u003C\u002Fh2>\u003Cp>The important question is not whether TurboQuant works in a benchmark. It is whether inference teams use it to cap memory bills or to push context limits higher. Based on how AI products have evolved so far, I would bet on the second path.\u003C\u002Fp>\u003Cp>If that happens, memory demand keeps climbing, but the shape of that demand changes. More of it gets tied to long-context serving, agent memory, and retrieval systems rather than only model weights. That is a more complicated market, and it is exactly the kind of market where a compression breakthrough can increase total spend.\u003C\u002Fp>\u003Cp>So the next thing to watch is simple: which vendors ship TurboQuant-like support first, and which products use the extra headroom to cross the one-million-token mark again. That will tell us whether the savings stay on the balance sheet or get poured back into bigger AI workloads.\u003C\u002Fp>","Google’s TurboQuant can cut KV-cache memory use 6x, but longer contexts may keep DRAM and NAND demand climbing.","www.theregister.com","https:\u002F\u002Fwww.theregister.com\u002F2026\u002F04\u002F01\u002Fgoogles_turboquant_reality\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775132152400-1kew.png",[13,14,15,16,17],"TurboQuant","KV cache","DRAM","NAND","AI inference","en",1,false,"2026-04-02T12:15:32.095995+00:00","2026-04-02T12:15:32.078+00:00","done","b64b796d-d6be-40c3-9f12-9a02281e2cb3","turboquant-wont-fix-memory-crunch-en","research","9d1ed0f2-aace-46ce-9b0a-0c0d8655e8e8","published","2026-04-08T09:00:52.221+00:00",[31,33,35,37,39],{"name":14,"slug":32},"kv-cache",{"name":15,"slug":34},"dram",{"name":16,"slug":36},"nand",{"name":13,"slug":38},"turboquant",{"name":17,"slug":40},"ai-inference",{"id":27,"slug":42,"title":43,"language":44},"turboquant-wont-fix-memory-crunch-zh","TurboQuant 解不了記憶體荒","zh",[46,52,58,64,70,76],{"id":47,"slug":48,"title":49,"cover_image":50,"image_url":50,"created_at":51,"category":26},"94994abd-e24d-4fd1-b941-942d03d19acf","turboquant-seo-shift-small-sites-en","TurboQuant and the SEO Shift for Small Sites","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778840455122-jfce.png","2026-05-15T10:20:28.134545+00:00",{"id":53,"slug":54,"title":55,"cover_image":56,"image_url":56,"created_at":57,"category":26},"670a7f69-911f-41e8-a18b-7d3491253a19","turboquant-vllm-comparison-fp8-kv-cache-en","TurboQuant vs FP8: vLLM’s first broad test","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778839858405-b5ao.png","2026-05-15T10:10:37.219158+00:00",{"id":59,"slug":60,"title":61,"cover_image":62,"image_url":62,"created_at":63,"category":26},"5aef1c57-961f-49f7-8277-f83f7336799a","llmbda-calculus-agent-safety-rules-en","LLMbda calculus gives agents safety rules","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778825459914-obkf.png","2026-05-15T06:10:36.242145+00:00",{"id":65,"slug":66,"title":67,"cover_image":68,"image_url":68,"created_at":69,"category":26},"712a0357-f7cd-48f2-adde-c2691da0815f","low-complexity-beamspace-denoiser-mmwave-mimo-en","A simpler beamspace denoiser for mmWave MIMO","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778814646705-e7mx.png","2026-05-15T03:10:31.764301+00:00",{"id":71,"slug":72,"title":73,"cover_image":74,"image_url":74,"created_at":75,"category":26},"f595f949-6ea1-4b0e-a632-f1832ef26e36","ai-benchmark-wins-cyber-scare-defenders-en","Why AI benchmark wins in cyber should scare defenders","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778807444539-gz7f.png","2026-05-15T01:10:30.04579+00:00",{"id":77,"slug":78,"title":79,"cover_image":80,"image_url":80,"created_at":81,"category":26},"3ad202d1-9e5f-49c5-8383-02fcf1a23cf2","why-linux-security-needs-patch-wave-mindset-en","Why Linux security needs a patch-wave mindset","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778741441493-ikl6.png","2026-05-14T06:50:25.906256+00:00",[83,88,93,98,103,108,113,118,123,128],{"id":84,"slug":85,"title":86,"created_at":87},"a2715e72-1fe8-41b3-abb1-d0cf1f710189","ai-predictions-2026-big-changes-en","AI Predictions for 2026: Brace for Big Changes","2026-03-26T01:25:07.788356+00:00",{"id":89,"slug":90,"title":91,"created_at":92},"8404bd7b-4c2f-4109-9ec4-baf29d88af2b","ml-papers-of-the-week-github-research-desk-en","ML Papers of the Week Turns GitHub Into a Research Desk","2026-03-27T01:11:39.480259+00:00",{"id":94,"slug":95,"title":96,"created_at":97},"87897a94-8065-4464-a016-1f23e89e17cc","ai-ml-conferences-to-watch-in-2026-en","AI\u002FML Conferences to Watch in 2026","2026-03-27T01:51:54.184108+00:00",{"id":99,"slug":100,"title":101,"created_at":102},"6f1987cf-25f3-47a4-b3e6-db0997695be8","openclaw-agents-manipulated-self-sabotage-en","OpenClaw Agents Can Be Manipulated Into Failure","2026-03-28T03:03:18.899465+00:00",{"id":104,"slug":105,"title":106,"created_at":107},"a53571ad-735a-4178-9f93-cb09b699d99c","vega-driving-language-instructions-en","Vega: Driving with Natural Language Instructions","2026-03-28T14:54:04.698882+00:00",{"id":109,"slug":110,"title":111,"created_at":112},"a34581d6-f36e-46da-88bb-582fb3e7425c","personalizing-autonomous-driving-styles-en","Drive My Way: Personalizing Autonomous Driving Styles","2026-03-28T14:54:26.148181+00:00",{"id":114,"slug":115,"title":116,"created_at":117},"2bc1ad7f-26ce-4f02-9885-803b35fd229d","training-knowledge-bases-writeback-rag-en","Training Knowledge Bases with WriteBack-RAG","2026-03-28T14:54:45.643433+00:00",{"id":119,"slug":120,"title":121,"created_at":122},"71adc507-3c54-4605-bbe2-c966acd6187e","packforcing-long-video-generation-en","PackForcing: Efficient Long-Video Generation Method","2026-03-28T14:55:02.646943+00:00",{"id":124,"slug":125,"title":126,"created_at":127},"675942ef-b9ec-4c5f-a997-381250b6eacb","pixelsmile-facial-expression-editing-en","PixelSmile Framework Enhances Facial Expression Editing","2026-03-28T14:55:20.633463+00:00",{"id":129,"slug":130,"title":131,"created_at":132},"6954fa2b-8b66-4839-884b-e46f89fa1bc3","adaptive-block-scaled-data-types-en","IF4: Smarter 4-Bit Quantization That Adapts to Your Data","2026-03-31T06:00:36.65963+00:00"]