Verified80% confidenceFactExact time
Continuous batching immediately removes completed sequences and inserts new requests after each decoding step, raising GPU utilization from 30–50% in traditional approaches to over 80%.
4
Sources
80%
Confidence
Long-term
Relevance
8/14/2026
First Seen
Sources
PagedAttention原理详解：GPU显存优化与模型路由技术
Related Claims
UnverifiedTraditional autoregressive generation can only produce one token at a time and requires a full forward pass, resulting in low GPU utilization, with the actual idle rate of compute units potentially exceeding 80%.75% similarUnverifiedThe Prefill stage is a compute-intensive task, with GPU utilization typically reaching 60–80%.74% similarUnverified每个中间推理token与输出token消耗等量的GPU算力，Extra High档位单次请求FLOPs可能是普通生成模式的数十倍73% similarUnverified许多推理服务的GPU利用率不足30%，通过VGPU技术可将利用率提升至70%以上72% similarUnverifiedTraditional LLM inference frameworks pre-allocate contiguous GPU memory for each request, leading to memory fragmentation, with actual GPU utilization often falling below 50%.71% similar
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