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Guidance and configs to enable expert-parallel communication overlap in Megatron-Bridge for MoE models — use to hide dispatch/combine latency and improve throug
Provides detailed guidance, example configs, and verification steps for enabling expert-parallel (EP) communication overlap in Megatron-Bridge. Describes dispatcher choices (alltoall vs flex), delayed weight-gradient computation, backend constraints (DeepEP/HybridEP), and minimal working configs to safely roll out overlap.
Use when running MoE models where expert dispatch/combine all-to-all communication is a measurable bottleneck and you have the memory and deployment constraints to tune for throughput. Avoid for tiny runs, early correctness bring-up, or incompatible PyTorch/TE/CUDA setups.
Engineers and agents with knowledge of deep-learning training infra (Megatron/Bridge) — useful for performance-tuning assistants and infra automation tools.
NVIDIA Megatron-Bridge expert-parallel overlap tuning skill. No scripts bundled. The SKILL.md body is null and the GitHub source URL returns 404, making the actual content inaccessible. Appears to be a broken or removed skill entry with only metadata (slug, tags, truncated description) available. Cannot verify any guidance content.
Skill content appears to have been removed or the URL is incorrect. The DB entry has metadata but no actual skill body. Recommend re-crawling or marking as broken.
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