Unverified50% confidenceFactExact time
The Decode stage is a memory-bandwidth-intensive task, with GPU utilization typically only 20–40%.
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9/10/2026
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UnverifiedThe 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 highest77% similarUnverifiedThe 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% 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70% similarUnverifiedGPU推理优化技术如投机解码（speculative decoding）、KV缓存复用对于将端到端延迟压缩到200毫秒以内至关重要70% similar
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