nemo-mbridge-perf-moe-dispatcher-selection
CommunityOptimizes MoE dispatcher choice by hardware.
Software Engineering#deepep#megatron-bridge#nvlink#hybridep#alltoall#moe-dispatcher#dispatcher-selection
Authorsayalinvidia
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Helps engineers select the optimal MoE token dispatcher (alltoall, DeepEP, or HybridEP) for Megatron Bridge workloads, reducing trial-and-error and aligning with hardware and EP configurations.
Core Features & Use Cases
- Hardware-aware dispatcher guidance: Recommends the best dispatcher per hardware (H100, GB200, GB300 NVL72) and EP degree.
- Back-end and tuning guidance: Documents the required flex backend settings (deepep, hybridep) and default SM tuning knobs, with notes on environment limitations.
- Use cases: Useful when bringing up MoE models, tracing regressions, or selecting configurations for Qwen3/DSV3 families.
Quick Start
Run the MoE dispatcher selector for your hardware and EP size to obtain the recommended dispatcher and default tuning knobs.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: nemo-mbridge-perf-moe-dispatcher-selection Download link: https://github.com/sayalinvidia/sayali-skills-test/archive/main.zip#nemo-mbridge-perf-moe-dispatcher-selection Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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