Unverified60% confidenceFactExact time
In traditional LLM inference, the KV Cache for each request requires pre-allocated contiguous GPU memory blocks; because generation length is unpredictable, memory must be reserved at the maximum length, resulting in actual GPU memory utilization that is typically below 30%.
2
Sources
60%
Confidence
Long-term
Relevance
8/13/2026
First Seen
Sources
Related Claims
VerifiedThe VRAM usage of KV Cache grows linearly with sequence length; for a 70-billion-parameter model using GQA, the KV Cache usage can reach tens of GB when processing 100,000 tokens.71% similarUnverifiedAccurately estimating a model's VRAM usage requires considering multiple dimensions including KV Cache, inference framework runtime overhead (typically an additional 0.5–2 GB), and activation values.69% similarUnverifiedThe VRAM consumption of the KV cache is proportional to context length, number of model layers, and number of attention heads; under an 8K context, a typical 27B model may consume an additional 2–4 GB of VRAM.69% similarUnverifiedFor million-token-level contexts, KV Cache can consume tens or even hundreds of gigabytes of GPU memory, becoming the primary bottleneck for long-context inference.69% similarUnverified长上下文在首次推理时需完整计算KV Cache，消耗GPU计算资源并占用显存带宽，拉长首token延迟（TTFT）69% similar
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