Verified80% confidenceFactTime unknown
For a model with 70B parameters, the KV Cache for an 8K context may occupy several additional gigabytes of VRAM
4
Sources
80%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
本地部署大模型怎么判断显存爆了？一文看懂显存监控方法
bilibili步步梅
Related Entities
Related Claims
UnverifiedThe KV Cache of a 70B-parameter model with 128K context support can require hundreds of gigabytes of VRAM at full load, exceeding the 80GB VRAM capacity of a single H100.82% similarUnverifiedWhen models with hundreds of billions of parameters process a 128K context window, KV Cache can occupy tens of gigabytes of VRAM.79% 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 VRAM79% 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.78% similarUnverifiedTraditional LLM inference engines have severe memory fragmentation issues when handling KV Cache; for a 13B parameter model, the KV Cache for a single request may occupy several GB of memory, while the actual utilization rate may be below 50%.77% similar
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