Unverified50% confidenceFactExact time
Separating Prefill and Decode to run on different resources is the mainstream architecture for optimizing resource utilization in current large-scale inference services.
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Long-term
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9/12/2026
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UnverifiedThe Prefill phase is a compute-intensive task with high demands on compute power; the Decode phase is a memory-access-intensive task that is sensitive to GPU memory bandwidth.74% similarUnverifiedIncreasing batch size can significantly improve decode-stage efficiency, because multiple requests share a single weight read.70% similarUnverified智能体文档工具本质是用token成本换取推理效率，通过预先压缩代码库信息节省后续每次任务的上下文开销70% similarUnverifiedDisaggregation technology splits the prefill and decode stages onto different hardware for processing.68% similarUnverifiedCurrent mainstream large models predominantly adopt a pure Decoder architecture, because its pretraining objective is highly compatible with generative dialogue tasks and scaling effects are more pronounced.67% similar
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