Unverified50% confidenceFactExact time
VRAM 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
1
Sources
50%
Confidence
Long-term
Relevance
7/22/2026
First Seen
Sources
用 Ollama 跑本地模型接入 Claude Code，AI 成本降低 99%
Related Claims
UnverifiedFor a typical 7B-parameter model, the KV Cache per 1K tokens requires approximately 0.5–1 GB of VRAM.77% 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.75% 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.72% similarUnverifiedTraditional 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%.70% similarUnverified上下文越长，模型需要缓存的 KV Cache 越大，KV Cache 使模型生成新 Token 时无需重新计算所有历史 Token 的注意力权重70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/593875API
curl https://kongchang.com/api/v1/knowledge/claims/593875MCP
get_claim(id=593875)