Unverified50% confidenceFactExact time
After vLLM starts, it occupies more than 90% of VRAM to build a cache pool in pursuit of maximum speed.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/16/2026
First Seen
Valid until: 12/15/2026
Sources
Related Entities
Related Claims
VerifiedCompared to traditional methods, PagedAttention enables vLLM to increase batch size by 2–4 times and achieve close to 100% memory utilization.71% similarUnverifiedvLLM's PagedAttention splits the KV cache into fixed-size physical blocks (typically 16 tokens), improving GPU memory utilization from below 40% to close to 90%.70% similarUnverifiedDocker部署vLLM/SGLang过程中会产生大量缓存，几个小时就能写入100GB以上66% similarUnverifiedUnder 4-bit and even 3-bit quantization, many models can still maintain more than 90% of their original performance.64% similarUnverified主流量化方案包括GPTQ、AWQ和QLoRA，可将模型压缩4-8倍同时保持95%以上下游任务性能64% similar
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