Verified75% confidenceFactExact time
Traditional 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 utilization
3
Sources
75%
Confidence
Long-term
Relevance
9/18/2026
First Seen
Sources
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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。80% similarVerifiedCPU+GPU hybrid inference uses a layer-level offloading mechanism, controlling the number of offloaded layers via the n_gpu_layers parameter; the main bottleneck is PCIe bandwidth latency, which typically reduces generation speed to 1/3 to 1/2 of pure GPU inference.77% similarVerifiedThe full-stack GPU paradigm keeps environment state, dynamics simulation, policy network forward passes, and gradient computation entirely within video memory, eliminating the data transfer bottleneck between CPU and GPU.77% similarUnverifiedColocate scheduling places rollout and training on the same set of GPUs to execute alternately, which causes them to contend for VRAM and compute and requires strict serialization.76% similarUnverifiedGPU采用隐藏延迟的策略，通过同时调度成千上万个线程，在一批线程因等待内存阻塞时切换到就绪线程76% similar
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