Unverified50% confidenceFactExact time
Each global layer of Spark X 2.5 stores approximately 4 KB of KV cache per token; across 9 layers this amounts to approximately 37 KB per token, reducing KV cache usage to approximately one-quarter of the approach where every layer records the full text
1
Sources
50%
Confidence
Long-term
Relevance
9/27/2026
First Seen
Sources
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Related Claims
UnverifiedFor a typical 7B-parameter model, the KV Cache per 1K tokens requires approximately 0.5–1 GB of VRAM.73% similarVerifiedThe VRAM usage of KV Cache grows linearly with sequence length; for a 70-billion-parameter model using GQA, the KV Cache usage can reach tens of GB when processing 100,000 tokens.70% similarUnverifiedWith KV Cache, each round of Q projection only needs to compute a 1×D_k vector, whereas without caching, a full N×D_k matrix must be computed.69% similarVerifiedLLaMA-2 7B's KV Cache occupies approximately 2 GB with a 4096-token context, and can surge to over 16 GB when extended to a 32K context67% similarUnverifiedVRAM 成为本地 LLM 上下文瓶颈的根本原因在于 KV Cache 机制，7B 模型每 1K token 的 KV Cache 约需 0.5–1GB 显存67% similar
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