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Fused MoE kernels combine routing and expert computation into GPU operations
The post describes fused MoE operations that combine token routing, data shuffling, and expert computation, and discusses hardware-specific kernel optimizations. Efficient MoE kernels can directly affect inference latency and cost, though the excerpt gives no performance results.
- What
- Fused MoE kernels combine token routing, data shuffling, and expert calculations into one batched GPU operation.
- How
- That replaces separate sequential loops; Hopper WGMMA's fixed M=64 shape can leave lanes idle when decode batches have fewer than 64 tokens.
- Why it matters
- Because MoE operations consume a large share of compute time, kernel efficiency affects request latency and cost.
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