Verified65% confidenceFactExact time
When models with hundreds of billions of parameters process a 128K context window, KV Cache can occupy tens of gigabytes of VRAM.
3
Sources
65%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
GPT-5.6 Trio Released: A Deep Dive into Sol/Terra/Luna and the Breakthrough in Design Judgment
redditr/singularity7/9/2026
Related Claims
Unverified一个200K上下文窗口的请求，其KV Cache可能占据数十GB显存84% similarVerifiedWhen the context length is extended to one million tokens, the KV Cache itself can occupy tens of gigabytes of VRAM.81% 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 VRAM81% similarUnverified128K指模型的上下文窗口大小，即模型单次可以处理约128,000个Token的输入信息81% similarVerifiedA unified memory pool of 128GB means that quantized models at the 70-billion-parameter scale or larger can be run locally.80% similar
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