Unverified70% confidenceFactExact time
Some of the latest systems, such as Sarathi-Serve and Splitwise, have proposed a Prefill-Decode disaggregation architecture that schedules the prefill and decode stages to different GPU clusters.
2
Sources
70%
Confidence
Long-term
Relevance
8/14/2026
First Seen
Sources
NVIDIA开源Switchyard：用Rust打造的AI任务调度引擎
Related Claims
Unverifiedprefill 与 decode 对算力和带宽的偏好不同，混用异构 GPU 机型虽然可行，但会让容量规划变得复杂。70% similarVerifiedTraditional inference runs the Prefill stage and Decode stage serially on the same group of GPUs, causing the two types of workloads to interfere with each other and making it difficult to optimize GPU utilization68% similarUnverifiedPD分离架构允许不同阶段选用不同规格的GPU实例，并可独立扩缩Prefill或Decode节点数量68% similarUnverifiedNVIDIA's proposed Encode-Prefill-Decode (EPD) disaggregation technology improves multimodal model serving efficiency by decoupling the visual encoder from the prefill and decode stages and deploying and scheduling it independently.67% similarUnverifiedMulti-node management is architecturally similar to lightweight GPU cluster scheduling, aggregating computing power from multiple machines through a unified control plane and distributing inference tasks on demand.64% similar
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