Expired50% confidenceFactExact time
In vLLM, each memory page typically stores KV vectors for a fixed number of tokens (e.g., 16), allowing KV Cache from different requests to be stored non-contiguously in physical memory.
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
UnverifiedvLLM的PagedAttention将KV缓存切分为固定大小物理块（通常16个token），使显存利用率从不足40%提升至接近90%75% similarUnverifiedvLLM引入PagedAttention技术将KV Cache分割为固定大小物理块按需分配，而llama.cpp采用预分配策略在模型加载时一次性保留完整KV Cache空间75% similarUnverifiedTaking the LLaMA-2 70B model as an example, a sequence of 2048 tokens requires approximately 4 GB of VRAM for the KV cache alone.75% similarUnverifiedvLLM compresses the KV Cache memory fragmentation rate from above 60% to near zero through PagedAttention technology, and can increase inference throughput by several times in high-concurrency scenarios.71% similarUnverifiedFor a model with 70B parameters, the KV Cache for an 8K context may occupy several additional gigabytes of VRAM70% similar
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