Unverified50% confidenceFactExact time
The available memory and VRAM of a local model directly determine the obtainable context window length, because inference requires maintaining a KV Cache in VRAM.
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
UnverifiedThe memory of LLMs depends on the context window, which is the maximum number of tokens the model can process in a single inference.75% similarUnverifiedThe VRAM occupied by the context window is calculated separately from the model weights, and KV cache VRAM usage grows linearly with context length.73% similarUnverifiedContext caching technology relies on the KV Cache mechanism at its core and is one of the key optimizations in mainstream inference frameworks such as vLLM and TensorRT-LLM72% similarUnverified决定大模型推理性能的是VRAM（显存）而非系统内存，当模型权重超出VRAM容量时推理速度会从数十TPS断崖式跌至个位数70% similarVerifiedThe autoregressive generation process of reasoning models is strictly sequential; KV Cache VRAM usage grows linearly with the length of the thinking token sequence, making memory bandwidth the performance bottleneck.70% similar
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