nemo-mbridge-perf-moe-optimization-workflow
Systematic workflow for MoE training optimization in Megatron Bridge, based on the Megatron-Core MoE paper. Covers the Three Walls framework, parallel folding, recompute strategy, dispatcher choice, and CUDA-graph bring-up.
npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-optimization-workflow --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MoE Training Optimization Workflow Stable docs: @docs/training/moe-optimization.md Card: @skills/nemo-mbridge-perf-moe-optimization-workflow/card.yaml Source: [Scalable Training of MoE Models with Megatron Core](https://arxiv.org/abs/2603.07685) ## Quick Reference Think in terms of the paper's Three Walls: - memory wall - communication wall - compute and host-overhead wall MoE tuning is iterativ
What does the nemo-mbridge-perf-moe-optimization-workflow skill do?
Systematic workflow for MoE training optimization in Megatron Bridge, based on the Megatron-Core MoE paper. Covers the Three Walls framework, parallel folding, recompute strategy, dispatcher choice, and CUDA-graph bring-up.
How do I install it?
Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-optimization-workflow --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.