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

Prince Canuma (@Prince_Canuma)

MLX에 TriAttention을 구현한 결과가 공개됐습니다. Gemma-4-31B-IT에서 BF16 기준 6만 토큰까지 KV 캐시를 최대 81% 압축할 수 있다고 합니다. TurboQuant처럼 KV 값을 양자화하는 대신, TriAttention은 중요도가 낮은 토큰을 아예 제거하는 방식입니다.

https://x.com/Prince_Canuma/status/2042021304270819394

#mlx #triaattention #gemma #kvcache #quantization

#AIEngineering #llm #kvcache #quantization

https://pub.towardsai.net/the-100-billion-selloff-started-by-a-12-page-pdf-from-two-google-researchers-ea88cf8451a5

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

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

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

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?

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 #deepseek #technology

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 #tech

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.

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 #tech

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