Verified65% confidenceFactExact time
The Prefill stage of LLM inference is compute-intensive with GPU utilization close to 100%, while the Decode stage is memory-bandwidth-intensive with the bottleneck being HBM VRAM bandwidth.
3
Sources
65%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
生产级LLM部署指南：模型选型、GPU配置与推理引擎全解析
Related Claims
UnverifiedThe Prefill (prompt processing) phase is compute-intensive and relies on the GPU's parallel matrix computation capability; the Decode (generation) phase is memory-bandwidth-intensive and depends more on VRAM bandwidth.79% similarUnverifiedThe prefill stage is a compute-intensive operation primarily limited by GPU compute, while the decode stage is a memory-bandwidth-intensive operation primarily limited by VRAM bandwidth.76% similarUnverifiedThe LLM output stage (decode) is limited by the serial nature of autoregression, resulting in lower GPU utilization than the batch parallel processing of the input stage (prefill), which causes the unit price per output token to typically be the highest76% similarUnverifiedThe Decode stage is a memory-bandwidth-intensive task, with GPU utilization typically only 20–40%.76% similar
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