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DeepSpeed-FastGen: Dynamic SplitFuse prompt/decode scheduling

Contribution grade

Language-model serving systems · Human-designed research method or resource

Reference score75,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Integrated DeepSpeed-MII/Inference with Dynamic SplitFuse to compose prompt and decoding work into better-balanced forward passes for throughput/latency tradeoffs.

Scientific scope

Integrated DeepSpeed-MII/Inference with Dynamic SplitFuse to compose prompt and decoding work into better-balanced forward passes for throughput/latency tradeoffs. Inherits Orca/PagedAttention/DeepSpeed and chunked scheduling ideas. Distinct from NVIDIA's diffusion FastGen. Workload/hardware-dependent throughput curves are not a universal speed claim or proof of an optimal production scheduler.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits Orca/PagedAttention/DeepSpeed and chunked scheduling ideas. Distinct from NVIDIA's diffusion FastGen. Workload/hardware-dependent throughput curves are not a universal speed claim or proof of an optimal production scheduler.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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