# nemo-mbridge-perf-moe-vlm-training

Practical guidance for training MoE VLMs in Megatron Bridge. It helps configure FSDP or 3D parallelism and debug OOM failures during multimodal training runs.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1800 installs · ★ 3735
- Author: NVIDIA
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-moe-vlm-training/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/nemo-mbridge-perf-moe-vlm-training

## What it does

This skill provides training heuristics from NVIDIA multimodal MoE experiments, including Qwen3-VL and Qwen3-Next. It documents when to start with FSDP versus 3D parallelism, covering configuration defaults on GB200 and B200 hardware.

The core recommendation is to stabilize model bring-up under FSDP first. Transitioning to 3D parallelism is only worthwhile if throughput headroom justifies the extra tuning overhead. The guide outlines settings for the HybridEP dispatcher, advises selective recompute over full recompute once the model fits, and restricts CUDA graphs to `attn moe_router moe_preprocess`.

Mock multimodal data without real images runs roughly twice as fast as real payloads, making mock throughput numbers misleading. Always benchmark end-to-end steps with actual image data before finalizing your parallel layout.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/nemo-mbridge-perf-moe-vlm-training/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/nemo-mbridge-perf-moe-vlm-training/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/nemo-mbridge-perf-moe-vlm-training/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/nemo-mbridge-perf-moe-vlm-training/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills nemo-mbridge-perf-moe-vlm-training
```

2. **Manually: put the files in place**
- File: `.github/skills/nemo-mbridge-perf-moe-vlm-training/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/nemo-mbridge-perf-moe-vlm-training
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/nemo-mbridge-perf-moe-vlm-training/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-moe-vlm-training/SKILL.md

## When to use it
- choose between FSDP and 3D parallelism for training MoE VLMs
- diagnose and resolve GPU out-of-memory errors in Megatron Bridge
- configure HybridEP dispatchers and CUDA graph scopes on GB200
- tune micro-batch size and recompute policies for vision pipelines

## Example prompts
- Help me decide between FSDP and 3D parallel for training Qwen3-VL on GB200.
- Investigate an OOM failure in our Megatron Bridge MoE VLM run and recommend recompute settings.
- What CUDA graph scope and dispatcher should I set for multimodal MoE training?

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: c9f7a42bf51751f3.

BEGIN THIRD-PARTY FILE c9f7a42bf51751f3
```text
---
name: nemo-mbridge-perf-moe-vlm-training
description: 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.
license: Apache-2.0
when_to_use: Training MoE VLMs, or investigating a commit that caused MoE VLM training failure or OOM; 'MoE VLM', 'multimodal MoE', 'Qwen3-VL training', 'FSDP vs 3D-parallel for VLM', 'MoE vision language model'.
---

# 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 time for deeper sweeps |

For MoE VLMs, the practical workflow is usually:

1. get the first reliable run with FSDP
2. stabilize real-data input, recompute, and memory behavior
3. move to 3D parallel only if the throughput headroom is worth the extra work

## Rounded Findings From Recent VLM Runs

### Qwen3-VL class models

The main patterns were consistent across the tracker:

- FSDP on GB200-class systems can already reach healthy high-teens utilization
  with a comparatively simple setup
- B200 FSDP runs are viable, but more sensitive to recompute choice and frozen
  vision settings
- 3D parallel can recover to a similar or better operating point, but only after
  tuning MBS, recompute, and the real vision path together

### Real data vs mock data

Mock-data VLM runs are not trustworthy performance proxies. In the experiments,
image-free mock runs looked closer to "roughly twice as fast" than "slightly
optimistic" when compared with real multimodal input.

Use real or realistic image payloads before drawing any conclusion about VLM
throughput.

### Smaller multimodal MoE runs

The smaller Qwen3.5-style multimodal experiments reinforce the same lessons:

- HybridEP is a solid default on GB200
- TE-scoped CUDA graphs help once the training loop is stable
- larger MBS can pay off, but only if the vision encoder does not become the
  next bottleneck

## Decision Guide

### Choose FSDP when

- you are bringing up a new VLM for the first time
- the model has awkward stage boundaries across embedding, vision, and decoder
- memory fit matters more than absolute throughput
- you may freeze the vision stack during decoder-focused tuning

### Choose 3D parallel when
```
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First 2571 of 5141 bytes. © NVIDIA · Apache-2.0 · https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-moe-vlm-training/SKILL.md

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