#kvcache — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #kvcache, aggregated by home.social.
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KV Cache Compression 900000x Beyond TurboQuant and Per-Vector Shannon Limit
https://arxiv.org/abs/2604.15356
#HackerNews #KVCache #Compression #TurboQuant #ShannonLimit #DataCompression
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KV-Cache в LLM: разбираем инференс через 9 ключевых вопросов
Почему Cache Read и Cache Write стоят денег и как работает Prompt Caching? Разбираем KV-Cache через 9 ключевых вопросов. Разобраться
https://habr.com/ru/articles/1021832/
#машинное_обучение #машинное_обучение_нейросети #llm #gpu #transformers #kvcache #prompt_caching #attention #vllm #prefix_caching
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From 300KB to 69KB per Token: How LLM Architectures Solve the KV Cache Problem
https://news.future-shock.ai/the-weight-of-remembering/
#HackerNews #LLMarchitectures #KVcache #AIoptimization #technews
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The key takeaway isn’t just compression—it’s where the bottleneck shifts. KV cache has been dominating memory footprint in long-context inference, so reducing it changes the cost structure significantly. But it doesn’t remove the constraint entirely.
#AI #ArtificialIntelligence #TurboQuant #Google #AIMemoryWall #AICompression #KVCache #LLMInference #AIInfrastructure #MemoryBottleneck #ModelEfficiency #AIHardware #DataCenter
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The key takeaway isn’t just compression—it’s where the bottleneck shifts. KV cache has been dominating memory footprint in long-context inference, so reducing it changes the cost structure significantly. But it doesn’t remove the constraint entirely:
https://www.buysellram.com/blog/will-googles-turboquant-ai-compression-finally-demolish-the-ai-memory-wall/#AI #ArtificialIntelligence #TurboQuant #Google #AIMemoryWall #AICompression #KVCache #LLMInference #AIInfrastructure #MemoryBottleneck #ModelEfficiency #AIHardware #DataCenter #technology
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The AI world is buzzing over TurboQuant, Google Research’s new answer to the AI Memory Wall. This isn't just an incremental update; it’s a fundamental shift in how we think about hardware efficiency.
By combining two new methods—PolarQuant and QJL—Google has managed to compress the Key-Value (KV) cache by 6x with zero accuracy loss. For those running H100s, this translates to an 8x speedup in attention processing.
Why it matters:
Beyond Brute Force: Much like DeepSeek-R1, Google is proving that high-level math can bypass the need for endless HBM expansion.
The "Memory Wall" Pivot: TurboQuant moves the bottleneck from memory bandwidth to compute, effectively "stretching" the life of existing silicon.
The Jevons Paradox: History shows that when we make a resource (memory) 6x more efficient, we don't use less of it—we build models 10x larger.
Is this the end of the global DRAM shortage, or just the beginning of a much larger scaling era?
#AI #ArtificialIntelligence #TurboQuant #Google #AIMemoryWall #AICompression #KVCache #LLMInference #AIInfrastructure #MemoryBottleneck #ModelEfficiency #AIHardware #DataCenter #deepseek #technology
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Google’s TurboQuant is being positioned as a breakthrough that could finally break the AI “memory wall”—but the reality is more nuanced.
In this analysis, we explore how TurboQuant achieves up to 6× memory reduction and 8× performance gains by compressing KV cache during inference, enabling more efficient use of existing GPUs like A100 and H100.
The upside is clear: lower infrastructure costs, extended hardware lifecycles, and the potential to run long-context AI workloads on more affordable systems. However, compression is not a silver bullet. The compute overhead of decompression, the persistent weight memory requirements, and the long-term effects of the Jevons Paradox suggest that demand for high-performance hardware is far from over.
#AI #ArtificialIntelligence #TurboQuant #Google #AIMemoryWall #AICompression #KVCache #LLMInference #AIInfrastructure #MemoryBottleneck #ModelEfficiency #AIHardware #DataCenter #tech
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Google’s TurboQuant is being positioned as a breakthrough that could finally break the AI “memory wall”—but the reality is more nuanced.
In this analysis, we explore how TurboQuant achieves up to 6× memory reduction and 8× performance gains by compressing KV cache during inference, enabling more efficient use of existing GPUs like A100 and H100.
https://www.buysellram.com/blog/will-googles-turboquant-ai-compression-finally-demolish-the-ai-memory-wall/#AI #TurboQuant #Google #AIMemoryWall #AICompression #KVCache #ModelEfficiency #AIHardware #DataCenter #technology
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Google's TurboQuant Compresses AI Memory by 6x — With Zero Accuracy Loss
https://techlife.blog/posts/google-turboquant
#Google #TurboQuant #LLM #AIEfficiency #KVCache #ICLR2026 #MachineLearning #Compression
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The Hidden Engineering Behind Fast AI: How LLM Inference Actually Works
https://techlife.blog/posts/llm-inference-optimization/
#LLM #Inference #PagedAttention #vLLM #FlashAttention #SpeculativeDecoding #MachineLearning #GPUOptimization #KVCache
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NVIDIA’s Inference Context Memory Storage Platform, announced at CES 2026, marks a major shift in how AI inference is architected. Instead of forcing massive KV caches into limited GPU HBM, NVIDIA formalizes a hierarchical memory model that spans GPU HBM, CPU memory, cluster-level shared context, and persistent NVMe SSD storage.
This enables longer-context and multi-agent inference by keeping the most active KV data in HBM while offloading less frequently used context to NVMe—expanding capacity without sacrificing performance. This shift also has implications for AI infrastructure procurement and the secondary GPU/DRAM market, as demand moves toward higher bandwidth memory and context-centric architectures.
https://www.buysellram.com/blog/nvidia-unveils-the-inference-context-memory-storage-platform/
#NVIDIA #Rubin #AI #Inference #LLM #AIInfrastructure #MemoryHierarchy #HBM #NVMe #DPU #BlueField4 #AIHardware #GPU #DRAM #KVCache #LongContextAI #DataCenter #AIStorage #AICompute #AIEcosystem #tech
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NVIDIA’s Inference Context Memory Storage Platform, announced at CES 2026, marks a major shift in how AI inference is architected. Instead of forcing massive KV caches into limited GPU HBM, NVIDIA formalizes a hierarchical memory model that spans GPU HBM, CPU memory, cluster-level shared context, and persistent NVMe SSD storage.
This enables longer-context and multi-agent inference by keeping the most active KV data in HBM while offloading less frequently used context to NVMe—expanding capacity without sacrificing performance. This shift also has implications for AI infrastructure procurement and the secondary GPU/DRAM market, as demand moves toward higher bandwidth memory and context-centric architectures.
https://www.buysellram.com/blog/nvidia-unveils-the-inference-context-memory-storage-platform/
#NVIDIA #Rubin #AI #Inference #LLM #AIInfrastructure #MemoryHierarchy #HBM #NVMe #DPU #BlueField4 #AIHardware #GPU #DRAM #KVCache #LongContextAI #DataCenter #AIStorage #AICompute #AIEcosystem #tech
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NVIDIA’s Inference Context Memory Storage Platform, announced at CES 2026, marks a major shift in how AI inference is architected. Instead of forcing massive KV caches into limited GPU HBM, NVIDIA formalizes a hierarchical memory model that spans GPU HBM, CPU memory, cluster-level shared context, and persistent NVMe SSD storage.
This enables longer-context and multi-agent inference by keeping the most active KV data in HBM while offloading less frequently used context to NVMe—expanding capacity without sacrificing performance. This shift also has implications for AI infrastructure procurement and the secondary GPU/DRAM market, as demand moves toward higher bandwidth memory and context-centric architectures.
https://www.buysellram.com/blog/nvidia-unveils-the-inference-context-memory-storage-platform/
#NVIDIA #Rubin #AI #Inference #LLM #AIInfrastructure #MemoryHierarchy #HBM #NVMe #DPU #BlueField4 #AIHardware #GPU #DRAM #KVCache #LongContextAI #DataCenter #AIStorage #AICompute #AIEcosystem #tech
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NVIDIA’s Inference Context Memory Storage Platform, announced at CES 2026, marks a major shift in how AI inference is architected. Instead of forcing massive KV caches into limited GPU HBM, NVIDIA formalizes a hierarchical memory model that spans GPU HBM, CPU memory, cluster-level shared context, and persistent NVMe SSD storage.
This enables longer-context and multi-agent inference by keeping the most active KV data in HBM while offloading less frequently used context to NVMe—expanding capacity without sacrificing performance. This shift also has implications for AI infrastructure procurement and the secondary GPU/DRAM market, as demand moves toward higher bandwidth memory and context-centric architectures.
https://www.buysellram.com/blog/nvidia-unveils-the-inference-context-memory-storage-platform/
#NVIDIA #Rubin #AI #Inference #LLM #AIInfrastructure #MemoryHierarchy #HBM #NVMe #DPU #BlueField4 #AIHardware #GPU #DRAM #KVCache #LongContextAI #DataCenter #AIStorage #AICompute #AIEcosystem #tech
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NVIDIA’s Inference Context Memory Storage Platform, announced at CES 2026, marks a major shift in how AI inference is architected. Instead of forcing massive KV caches into limited GPU HBM, NVIDIA formalizes a hierarchical memory model that spans GPU HBM, CPU memory, cluster-level shared context, and persistent NVMe SSD storage.
This enables longer-context and multi-agent inference by keeping the most active KV data in HBM while offloading less frequently used context to NVMe—expanding capacity without sacrificing performance. This shift also has implications for AI infrastructure procurement and the secondary GPU/DRAM market, as demand moves toward higher bandwidth memory and context-centric architectures.
https://www.buysellram.com/blog/nvidia-unveils-the-inference-context-memory-storage-platform/
#NVIDIA #Rubin #AI #Inference #LLM #AIInfrastructure #MemoryHierarchy #HBM #NVMe #DPU #BlueField4 #AIHardware #GPU #DRAM #KVCache #LongContextAI #DataCenter #AIStorage #AICompute #AIEcosystem #tech
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NVIDIA’s new Inference Context Memory Storage Platform reshapes AI inference by treating KV cache as a multi-tier memory hierarchy—from HBM to NVMe SSD. This enables longer context windows, persistent reasoning, and scalable multi-agent inference while keeping hot data in GPU memory and offloading cold context to SSD.
https://www.buysellram.com/blog/nvidia-unveils-the-inference-context-memory-storage-platform/
#NVIDIA #Rubin #AI #Inference #LLM #AIInfrastructure #MemoryHierarchy #HBM #NVMe #DPU #BlueField4 #AIHardware #GPU #DRAM #KVCache #DataCenter #tech -
#Nvidia's new #KVcache system is creating significant discussion within the industry, particularly regarding its overlap with #datastorage partners like #NetApp. Analysts are expressing concerns that this development may exacerbate the existing #memoryshortage, potentially resulting in increased prices for #enterpriseIT buyers.