nemo-mbridge-perf-moe-vlm-training
Practical guidance for training MoE VLMs in Megatron Bridge. Compares FSDP and 3D-parallel approaches, using rounded lessons from Qwen3-VL, Qwen3-Next, and other multimodal experiments.
npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training --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 VLM Training Stable docs: @docs/training/moe-optimization.md Card: @skills/nemo-mbridge-perf-moe-vlm-training/card.yaml ## FSDP vs 3D Parallel | Approach | Strength | Best fit | |---|---|---| | FSDP | Simplest path to a working multimodal run | first bring-up, memory-first tuning, awkward PP boundaries | | 3D parallel | Higher ceiling after tuning | stable models with a clean PP layout and t
What does the nemo-mbridge-perf-moe-vlm-training skill do?
Practical guidance for training MoE VLMs in Megatron Bridge. Compares FSDP and 3D-parallel approaches, using rounded lessons from Qwen3-VL, Qwen3-Next, and other multimodal experiments.
How do I install it?
Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training --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.