Unverified50% confidenceFactExact time
Through optimization of the KV Cache, the cache required by DeepSeek per token has dropped to the 1/1000 level, and model memory and SSD requirements have been reduced to one-quarter and one-eighth of the previous generation respectively.
1
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50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
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Related Claims
UnverifiedTraditional LLM inference engines have severe memory fragmentation issues when handling KV Cache; for a 13B parameter model, the KV Cache for a single request may occupy several GB of memory, while the actual utilization rate may be below 50%.73% similarUnverifiedvLLM compresses the KV Cache memory fragmentation rate from above 60% to near zero through PagedAttention technology, and can increase inference throughput by several times in high-concurrency scenarios.72% similarUnverifiedPrompt缓存（KV Cache）在相同系统提示多次使用时可复用缓存的KV矩阵，通常将延迟降低40-60%、成本降低50-90%71% similar
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