Graphsignal
graphsignal/graphsignal
Profile AI inference workloads (vLLM, SGLang, TensorRT-LLM, PyTorch, any GPU application) with the Graphsignal profiler and read the results from its local /signals JSON endpoint.
Practical guidance for training MoE VLMs in Megatron Bridge.
Install Nemo Mbridge Perf Moe Vlm Training
$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-vlm-training --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-vlm-training .claude/skills/nemo-mbridge-perf-moe-vlm-training && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "nemo-mbridge-perf-moe-vlm-training" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-vlm-training into .claude/skills/nemo-mbridge-perf-moe-vlm-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-vlm-training", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-vlm-trainingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-vlm-training --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-vlm-training .agents/skills/nemo-mbridge-perf-moe-vlm-training && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "nemo-mbridge-perf-moe-vlm-training" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-vlm-training into .agents/skills/nemo-mbridge-perf-moe-vlm-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-vlm-training", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-vlm-training --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-vlm-training .cursor/skills/nemo-mbridge-perf-moe-vlm-training && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "nemo-mbridge-perf-moe-vlm-training" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-vlm-training into .cursor/skills/nemo-mbridge-perf-moe-vlm-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-vlm-training", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/nemo-mbridge-perf-moe-vlm-training--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-vlm-training --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-vlm-training .gemini/skills/nemo-mbridge-perf-moe-vlm-training && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "nemo-mbridge-perf-moe-vlm-training" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-vlm-training into .gemini/skills/nemo-mbridge-perf-moe-vlm-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-vlm-training", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-vlm-trainingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-vlm-training .github/skills/nemo-mbridge-perf-moe-vlm-training && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "nemo-mbridge-perf-moe-vlm-training" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-vlm-training into .github/skills/nemo-mbridge-perf-moe-vlm-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-vlm-training", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-vlm-training --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/nemo-mbridge-perf-moe-vlm-training .opencode/skills/nemo-mbridge-perf-moe-vlm-training && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "nemo-mbridge-perf-moe-vlm-training" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-vlm-training into .opencode/skills/nemo-mbridge-perf-moe-vlm-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-vlm-training", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
nemo-mbridge-perf-moe-vlm-training- GitHub stars
- 3.6k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 615 words
- Files
- 6
- Skills in repo
- 390
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Practical guidance for training MoE VLMs in Megatron Bridge.
- Works in 3 steps: get the first reliable run with FSDP → stabilize real-data input, recompute,… → move to 3D parallel only if the…
- Tasks that involve Deep learning
- SKILL.md covers FSDP vs 3D Parallel, Rounded Findings From Recent…, Decision Guide and Key Tuning Knobs, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Nemo Mbridge Perf Moe Vlm Training is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. 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.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files (for example `BENCHMARK.md`, `card.yaml` and `evals/evals.json`).
It sits in AI & LLM Engineering, covering Deep learning. It works with NVIDIA AI Platform, Qwen and CUDA. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Deep learning
Example prompts
- “/nemo-mbridge-perf-moe-vlm-training”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- get the first reliable run with FSDP
- stabilize real-data input, recompute, and memory behavior
- move to 3D parallel only if the throughput headroom is worth the extra work
What it can do on your machine
Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Nemo Mbridge Perf Moe Vlm Training loads about 1.3k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 615 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 615 words, ~1,286 tokens.
Download SKILL.mdSave it as
.claude/skills/nemo-mbridge-perf-moe-vlm-training/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- 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…
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:
- get the first reliable run with FSDP
- stabilize real-data input, recompute, and memory behavior
- 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
- the model is already stable under FSDP
- the PP layout is clear and repeatable
- you can sweep MBS, recompute, and CUDA-graph scope together
- the goal is best steady-state throughput, not easiest bring-up
Show full SKILL.md (283 more words)Show less
Key Tuning Knobs
Freeze the vision stack when appropriate: if the work is decoder-focused, freezing the vision side often gives a small but real throughput gain and reduces memory pressure.
Sweep MBS aggressively: VLMs are more MBS-sensitive than text-only MoE runs because the vision path changes the compute-to-overhead balance.
Prefer selective recompute once the model fits: full recompute is a useful bring-up tool, but selective recompute is usually the better steady state.
Match CUDA-graph scope to the workload:
attn moe_router moe_preprocessis the safer MoE default, while narrower scopes can still be useful for controlled experiments.Use ETP only when EP alone is insufficient: it can unlock a layout, but it also introduces more communication and more tuning surface.
Representative Config Families
FSDP-first GB200 path
TP=1 CP=1 PP=1
EP sized to the expert topology, often large
Dispatcher: HybridEP on GB200-class systems
Recompute: start with full, then relax toward selective recompute3D-parallel GB200 path
TP=1 CP=1 PP=1 or modest PP
EP and ETP sized to the expert topology
Dispatcher: HybridEP
CUDA Graph: start narrow, then widen only after the real-data path is stableCompatibility
| Feature | FSDP | 3D parallel |
|---|---|---|
| HybridEP on GB200 | strong default | strong default once topology is stable |
| CUDA graphs | useful after bring-up | useful, but more scope-sensitive |
| Freeze vision | natural fit | possible, but less often used as the headline perf path |
| Selective recompute | recommended | recommended |
Pitfalls
Mock multimodal data is misleading: it can make the decoder look much healthier than the real end-to-end VLM path.
The vision encoder can dominate unexpectedly: profile encoder, projector, and decoder separately before attributing everything to the dispatcher.
Do not compare FSDP and 3D-parallel runs with different effective work: normalize by useful tokens and workload shape, not only by step time.
ETP is not free: use it as a fit or topology tool, not as the default.
Recompute and CUDA-graph choices are coupled: the setting that gets the model to fit is often not the setting that gives the best steady-state speed.
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 5 other files in skills/nemo-mbridge-perf-moe-vlm-training of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- card.yaml
- evals/evals.json
- skill-card.md
- skill.oms.sig
Open the folder on GitHubat commit 14a98ae
Compare with similar skills
Nemo Mbridge Perf Moe Vlm Training next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Nemo Mbridge Perf Moe Vlm Training this skillNVIDIA/skills | 3.6k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Graphsignalgraphsignal/graphsignal | 257 | — | ~6.3k | Automated safety check: Pass | Apache-2.0 | |
| Debug Cuda Crashsgl-project/sglang | 37k | 2 repos | ~4.9k | Automated safety check: Pass | Apache-2.0 | |
| Quark Env Preflightamd/Quark | 182 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Hyperpod Version Checkerawslabs/agent-plugins | 916 | — | ~910 | Automated safety check: Pass | Apache-2.0 | |
| Spark Environment Setupwshobson/agents | 40k | — | ~2k | Automated safety check: Pass | MIT |
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Questions about Nemo Mbridge Perf Moe Vlm Training
What does Nemo Mbridge Perf Moe Vlm Training do?
Practical guidance for training MoE VLMs in Megatron Bridge. Nemo Mbridge Perf Moe Vlm Training is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Practical guidance for training MoE VLMs in Megatron Bridge.
When should I use Nemo Mbridge Perf Moe Vlm Training?
Nemo Mbridge Perf Moe Vlm Training fits situations like: tasks that involve Deep learning.
How do I install Nemo Mbridge Perf Moe Vlm Training in Claude Code?
Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a claude-code`. Or copy the skill folder (skills/nemo-mbridge-perf-moe-vlm-training in NVIDIA/skills) into .claude/skills/nemo-mbridge-perf-moe-vlm-training in your project. Claude Code loads it when a task matches its description.
How do I install Nemo Mbridge Perf Moe Vlm Training in Codex?
Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a codex`. Or copy the skill folder (skills/nemo-mbridge-perf-moe-vlm-training in NVIDIA/skills) into .agents/skills/nemo-mbridge-perf-moe-vlm-training in your project. Codex loads it when a task matches its description.
Can I use Nemo Mbridge Perf Moe Vlm Training in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nemo-mbridge-perf-moe-vlm-training, .gemini/skills/nemo-mbridge-perf-moe-vlm-training, .github/skills/nemo-mbridge-perf-moe-vlm-training and .opencode/skills/nemo-mbridge-perf-moe-vlm-training in your project.
What does Nemo Mbridge Perf Moe Vlm Training need to run?
SKILL.md names no scripts, command-line tools or credentials: Nemo Mbridge Perf Moe Vlm Training is instructions for the agent only.
Does Nemo Mbridge Perf Moe Vlm Training access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Nemo Mbridge Perf Moe Vlm Training safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Nemo Mbridge Perf Moe Vlm Training use?
Nemo Mbridge Perf Moe Vlm Training is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Nemo Mbridge Perf Moe Vlm Training use?
About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Nemo Mbridge Perf Moe Vlm Training?
Skills that share tags, products or a category with Nemo Mbridge Perf Moe Vlm Training: Graphsignal (graphsignal/graphsignal, 257 stars), Debug Cuda Crash (sgl-project/sglang, 37k stars), Quark Env Preflight (amd/Quark, 182 stars) and Hyperpod Version Checker (awslabs/agent-plugins, 916 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Nemo Mbridge Perf Moe Vlm Training?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.