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[GH-ISSUE #484] [Green Context MoE] Introduce GC MoE #202

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opened 2026-10-26 21:14:41 +00:00 by bsakaguchi · 0 comments
bsakaguchi commented 2026-10-26 21:14:41 +00:00
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Originally created by @Bruce-Lee-LY on GitHub (Oct 26, 2026).
Original GitHub issue: https://github.com/deepseek-ai/DeepGEMM/issues/484

GC MoE is a CuTe DSL implementation of FP8/FP4 mixture-of-experts inference with CUDA Green Contexts. It overlaps expert-parallel token dispatch with Tensor Core computation, then combines the expert outputs through a three-role CUDA Graph. The operator supports routed experts and optional fused shared experts (SE), with FP8 activations, packed FP4 routed weights, FP8 shared weights and BF16 outputs.

In the decode scenario, the performance of gc moe is approximately 9% faster than that of mega moe on average.

gc-moe

Originally created by @Bruce-Lee-LY on GitHub (Oct 26, 2026). Original GitHub issue: https://github.com/deepseek-ai/DeepGEMM/issues/484 GC MoE is a CuTe DSL implementation of FP8/FP4 mixture-of-experts inference with CUDA Green Contexts. It overlaps expert-parallel token dispatch with Tensor Core computation, then combines the expert outputs through a three-role CUDA Graph. The operator supports routed experts and optional fused shared experts (SE), with FP8 activations, packed FP4 routed weights, FP8 shared weights and BF16 outputs. In the decode scenario, the performance of gc moe is approximately 9% faster than that of mega moe on average. [gc-moe](https://github.com/Bruce-Lee-LY/gc-moe)
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deepseek-ai/DeepGEMM#202
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