Expired50% confidenceFactExact time
Traditional 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%.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
vLLM Deep Dive: How PagedAttention Enables High-Throughput LLM Inference
githubvllm-project6/6/2026
Related Claims
UnverifiedFor a model with 70B parameters, the KV Cache for an 8K context may occupy several additional gigabytes of VRAM77% similarUnverifiedWhen models with hundreds of billions of parameters process a 128K context window, KV Cache can occupy tens of gigabytes of VRAM.77% similarUnverifiedIn long-context scenarios (such as models with a 128K token window), the GPU memory occupied by the KV Cache may exceed that of the model weights themselves.76% 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% similarVerifiedThe 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.73% similar
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