Unverified60% confidenceFactExact time
For a typical 7B-parameter model, the KV Cache per 1K tokens requires approximately 0.5–1 GB of VRAM.
2
Sources
60%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
用 Ollama 跑本地模型接入 Claude Code，AI 成本降低 99%
Related Claims
UnverifiedTaking a 7B parameter model as an example, each additional 1K tokens of context consumes approximately 0.5–1 GB of GPU memory.83% 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77% similarVerifiedFor a model with 70B parameters, the KV Cache for an 8K context may occupy several additional gigabytes of VRAM76% 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.76% similarVerifiedFor a typical 7B model with 32 layers and 32 attention heads, storing the KV Cache for each token requires approximately 0.5 MB of VRAM, so generating 1,000 tokens requires an additional ~500 MB of VRAM74% similar
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