Unverified50% confidenceFactExact time
FP8 KV cache is a quantization technique that compresses the Key-Value cache in the attention mechanism from FP16 or BF16 to 8 bits, which can halve the GPU memory usage of the KV cache
1
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50%
Confidence
Long-term
Relevance
9/7/2026
First Seen
Sources
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Related Claims
UnverifiedThe KV Cache (Key-Value Cache) strategy stores already-computed attention key-value pairs in GPU memory for reuse, which consumes a large amount of GPU memory resources.79% similarUnverifiedQuantized KV caches (such as storing KV vectors in FP8 or INT4) can compress the cache size to 1/2 or even 1/4 of the original.74% similarUnverifiedIn large model API call scenarios, cache hit rate refers to the reuse ratio of the KV Cache (key-value cache), where the model can directly read the attention key-value pairs of already-processed tokens from the cache.73% similarUnverifiedKV Cache reduces the computational complexity of self-attention from O(N²) to O(N) by caching already-computed Key-Value pairs in GPU memory, but memory usage grows linearly with sequence length.73% similarUnverifiedFor the LLaMA-2 70B model, when processing a context of 4096 tokens, the KV Cache requires approximately 20 GB of system memory at FP16 precision.71% similar
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