Google's TurboQuant and Kimi Linear Significantly Reduce LLM KV Cache Memory

Google's TurboQuant compresses KV cache to 3-bit, achieving a near-lossless 6x reduction in size, while Kimi Linear cuts KV cache by approximately 75% by replacing most full-attention layers with fixed O(1) linear attention. Combining these techniques can transform the KV cache memory wall into a manageable fraction of the FP16 baseline, especially for long contexts and high concurrency.

So What

LLM inference memory demand is shifting from fixed model weights to dynamic KV cache, enabling more concurrent users and longer contexts per GPU.

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Google's TurboQuant and Kimi Linear Significantly Reduce LLM KV Cache Memory

Semicon Twitter · Twitter · 15h ago

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Updated 15h ago · 1 source

Google's TurboQuant and Kimi Linear Significantly Reduce LLM KV Cache Memory

Google's TurboQuant compresses KV cache to 3-bit, achieving a near-lossless 6x reduction in size, while Kimi Linear cuts KV cache by approximately 75% by replacing most full-attention layers with fixed O(1) linear attention. Combining these techniques can transform the KV cache memory wall into a manageable fraction of the FP16 baseline, especially for long contexts and high concurrency.

So What

LLM inference memory demand is shifting from fixed model weights to dynamic KV cache, enabling more concurrent users and longer contexts per GPU.

View Tweet→