Unverified50% confidenceFactExact time
The 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 highest
1
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50%
Confidence
Long-term
Relevance
9/7/2026
First Seen
Sources
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Related Claims
UnverifiedThe 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.76% similarUnverifiedThe LLM decoding process is memory bound, with the GPU spending most of its time waiting for data transfers during token-by-token generation.73% similarUnverified自回归推理的decode阶段是纯粹的内存带宽受限操作，将模型从FP16压缩到1-bit理论上可将token生成速度提升16倍72% similar
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