Verified65% confidenceCautionExact time
When the context length is extended to one million tokens, the KV Cache itself can occupy tens of gigabytes of VRAM.
3
Sources
65%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
Related Claims
VerifiedWhen models with hundreds of billions of parameters process a 128K context window, KV Cache can occupy tens of gigabytes of VRAM.81% similarVerifiedTaking 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.81% similarUnverifiedThe computational cost of long-context attention grows quadratically with sequence length, and Key and Value vectors must be cached for each token (KV Cache), which at a 1M context can reach hundreds of GB.80% similarUnverified一个200K上下文窗口的请求，其KV Cache可能占据数十GB显存79% similarUnverifiedWhen the context length is extended from 4K to 8K, the VRAM occupied by the KV Cache nearly doubles79% similar
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