Unverified50% confidenceFactExact time
一个200K上下文窗口的请求，其KV Cache可能占据数十GB显存
1
Sources
50%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
UnverifiedWhen models with hundreds of billions of parameters process a 128K context window, KV Cache can occupy tens of gigabytes of VRAM.84% similarUnverifiedWhen the context length is extended to one million tokens, the KV Cache itself can occupy tens of gigabytes of VRAM.79% similarUnverifiedTaking LLaMA-2 70B as an example, when processing a 4096-token context with a batch size of 32, the KV Cache can consume over 80 GB of memory.75% similarUnverifiedThe latest large models have context windows of 128K or even 200K tokens, yet still face attention dilution issues when handling complex engineering tasks.75% similarUnverifiedFor 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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