Cross-Platform Fused MoE Dispatch in Triton: Portable Expert Routing Without CUDA [R]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
New preprint. A Mixture-of-Experts inference kernel (TritonMoE) written entirely in OpenAI Triton, targeting portability across NVIDIA and AMD without vendor-specific code.
Highlights:
- A fused gate+up GEMM computes both SwiGLU projections from shared tile loads, eliminating 35% of global memory traffic.
- 89-131% of Megablocks throughput at inference batch sizes (up to 512 tokens) on A100; the same kernel runs on MI300X unchanged.
- Limitations: falls behind at 2048+ tokens, and degrades with 64+ experts under extreme routing skew.
Paper: https://arxiv.org/abs/2605.23911
Code: https://github.com/bassrehab/triton-kernels
Writeup with benchmarks: https://subhadipmitra.com/blog/2026/fused-moe-dispatch-triton/
[link] [comments]
More from r/MachineLearning
-
Improving machine-translated novels via style transfer — looking for advice on the faithfulness/fluency tradeoff [P]
Jul 2
-
How papers are selected for Best Paper, Oral, or Highlight presentation at major ML/CV conferences such as CVPR, ICCV, ECCV, NeurIPS, and ICLR? [D]
Jul 2
-
BMVC 2026 Review Discussion Thread [D]
Jul 2
-
Has anyone tried this approach with Fast Byte Latent Transformers ? [R]
Jul 2
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.