Official agent skill

Nemo Mbridge Perf Moe Hardware Configs

Representative, point-in-time MoE training playbooks by hardware and model family.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Nemo Mbridge Perf Moe Hardware Configs

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-hardware-configs -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-hardware-configs --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ 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-hardware-configs .claude/skills/nemo-mbridge-perf-moe-hardware-configs && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "nemo-mbridge-perf-moe-hardware-configs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-hardware-configs into .claude/skills/nemo-mbridge-perf-moe-hardware-configs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-hardware-configs", 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

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-hardware-configs

Type 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.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-hardware-configs -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-hardware-configs --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-hardware-configs .agents/skills/nemo-mbridge-perf-moe-hardware-configs && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "nemo-mbridge-perf-moe-hardware-configs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-hardware-configs into .agents/skills/nemo-mbridge-perf-moe-hardware-configs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-hardware-configs", 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.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-hardware-configs -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-hardware-configs --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-hardware-configs .cursor/skills/nemo-mbridge-perf-moe-hardware-configs && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "nemo-mbridge-perf-moe-hardware-configs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-hardware-configs into .cursor/skills/nemo-mbridge-perf-moe-hardware-configs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-hardware-configs", 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

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/nemo-mbridge-perf-moe-hardware-configs

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-hardware-configs -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-hardware-configs --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-hardware-configs .gemini/skills/nemo-mbridge-perf-moe-hardware-configs && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "nemo-mbridge-perf-moe-hardware-configs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-hardware-configs into .gemini/skills/nemo-mbridge-perf-moe-hardware-configs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-hardware-configs", 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.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-hardware-configs

Installs 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).

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-hardware-configs -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ 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-hardware-configs .github/skills/nemo-mbridge-perf-moe-hardware-configs && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "nemo-mbridge-perf-moe-hardware-configs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-hardware-configs into .github/skills/nemo-mbridge-perf-moe-hardware-configs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-hardware-configs", 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.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-hardware-configs -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-moe-hardware-configs --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-hardware-configs .opencode/skills/nemo-mbridge-perf-moe-hardware-configs && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "nemo-mbridge-perf-moe-hardware-configs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-moe-hardware-configs into .opencode/skills/nemo-mbridge-perf-moe-hardware-configs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-moe-hardware-configs", 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-hardware-configs
GitHub stars: 3.6k
Token cost: ~2k tokens
SKILL.md length: 722 words
Files: 6
Skills in repo: 390
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Representative, point-in-time MoE training playbooks by hardware and model family.

Works in 7 steps: Do not cargo-cult a tracker row: the… → Container quality matters: large… → VPP must be intentional: a bad VPP split… → …
AI & LLM Engineering work in your project
SKILL.md covers Quick Platform Playbook, First Answer Checklist, Rounded Performance Bands and Representative Config Families, plus 2 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Nemo Mbridge Perf Moe Hardware Configs is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Representative, point-in-time MoE training playbooks by hardware and model family. Use them as candidate seeds, then revalidate the exact runtime, semantics, topology, and steady-state throughput.

Its SKILL.md is about 2k 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. It works with CUDA and Qwen. 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

AI & LLM Engineering work in your project

Example prompts

“/nemo-mbridge-perf-moe-hardware-configs”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

Do not cargo-cult a tracker row: the winning config usually depends on
Container quality matters: large regressions can come from the software
VPP must be intentional: a bad VPP split can erase the gain from a better
Compare absolute throughput, not only MFU: MFU can mislead when switching
Force-balance routing is benchmark-only: it can control routing variance,
Do not treat the dispatcher table as a hard platform rule: HybridEP is
Separate screening, causality, and acceptance: short runs reject weak

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 (its code samples are bash).
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 Hardware Configs loads about 2k tokens when it runs. Until then it costs about 59 tokens; SKILL.md has 722 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~59
When it runs · the whole SKILL.md, loaded when a task matches: ~2k

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). 722 words, ~1,984 tokens.

Download SKILL.mdSave it as .claude/skills/nemo-mbridge-perf-moe-hardware-configs/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-hardware-configs
description: Representative, point-in-time MoE training playbooks by hardware and model family. Use them as candidate seeds, then revalidate the exact runtime, semantics, topology, and steady-state throughput.
license: Apache-2.0
when_to_use: Hardware-specific MoE playbooks or throughput estimates; 'MoE on H100', 'GB200 config', 'expected throughput', 'MoE hardware playbook', 'parallelism for B200'.

MoE Hardware Configuration Reference

Stable docs: @docs/training/moe-optimization.md Card: @skills/nemo-mbridge-perf-moe-hardware-configs/card.yaml

Quick Platform Playbook

These rows are search seeds, not hardware defaults or throughput promises.

Platform	Candidates to screen after `alltoall` bring-up	What usually matters most
H100	DeepEP or HybridEP, explicit overlap, supported FP8 modes	communication overlap, dispatcher/runtime compatibility, and PP efficiency
B200	DeepEP or HybridEP, supported FP8 modes, careful PP layout	container quality and tuned communication settings
GB200	HybridEP, then profile-driven graphs and CPU cleanup	host overhead, topology-aware dispatch, memory headroom
GB300	HybridEP and the target container's lower-precision/kernel stack	the same system interactions as GB200, with remeasurement required

First Answer Checklist

For hardware playbook questions, answer from these canonical rows before adding throughput caveats:

Workload	Hardware	Dispatcher	Layout
DSV3	H100	DeepEP	TP=2, EP=64, PP=8, VPP=4
DSV3	GB200/GB300	HybridEP	TP=1, EP=64, PP=4, VPP=4
Qwen3 235B	H100	`alltoall` + overlap in the current canonical recipe	TP=2, EP=32, PP=8, VPP=4
Qwen3 235B	GB200	HybridEP	TP=1 or 2, EP=32-64, PP=4, VPP=unspecified
Qwen3 30B	16×H100	HybridEP	TP=1, EP=16, PP=1, plain EP overlap

For Qwen3 235B on GB200, explicitly say VPP=unspecified; do not invent or extrapolate VPP=12 unless a measured row provides it. Treat TE-scoped CUDA graph scopes (attn, moe_router, moe_preprocess) as profile-driven candidates, CUDA_DEVICE_MAX_CONNECTIONS selection, PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True, NCCL_GRAPH_REGISTER=0, GB200/GB300 CPU-side tuning, and the warning not to cargo-cult tracker rows.

Rounded Performance Bands

These are intentionally rounded so the document stays durable as the tracker moves. Treat them as planning ranges, not exact promises.

Workload family	Hardware	Typical band	Representative shape
DSV3, large-scale	H100	low-to-mid hundreds TFLOPS/GPU, high-teens MFU	TP2, EP64, PP8, DeepEP
DSV3, large-scale	B200	high-hundreds TFLOPS/GPU, mid-teens MFU	TP1, EP32, PP8, DeepEP
DSV3, large-scale	GB200	around 1K TFLOPS/GPU, low-20s MFU	TP1, EP64, PP4, HybridEP
DSV3, large-scale	GB300	above the GB200 band, often mid-20s MFU	TP1, EP64, PP4, HybridEP
Qwen3 235B	H100	historical low-300s snapshots; remeasure the current recipe	TP2, EP32, PP8; current recipe uses `alltoall` + overlap
Qwen3 235B	GB200	high-hundreds TFLOPS/GPU in tuned runs	TP1 or TP2, EP32-64, PP4, HybridEP
Qwen3 30B	H100	about 300 TFLOPS/GPU on the validated 16-GPU shape	TP1, EP16, PP1, HybridEP + EP overlap
Qwen3-Next 80B	GB200	low-300s TFLOPS/GPU in BF16-class runs	TP1, EP32, PP2, HybridEP

Representative Config Families

DSV3 on H100

text

Dispatcher: DeepEP
TP=2  EP=64  PP=8  VPP=4
Routing: force balance
Recompute: light-to-moderate selective recompute
Priority: overlap communication and keep PP efficient

DSV3 on B200

text

Dispatcher: DeepEP
TP=1  EP=32  PP=8  VPP=2 or similar
Precision: MXFP8-class
Recompute: selective recompute around MLA up-projection and MLP-side modules
Priority: container quality, PP layout, and DeepEP SMS tuning

DSV3 on GB200 or GB300

text

Dispatcher: HybridEP
TP=1  EP=64  PP=4  VPP=4
Precision: MXFP8-class
CUDA Graph: attn + moe_router + moe_preprocess
Priority: HybridEP, CPU optimization, and graph-friendly static shapes

Qwen3 235B on H100

text

Dispatcher: alltoall in the current canonical recipe; re-screen flex backends on the target stack
TP=2  EP=32  PP=8  VPP=4
Recompute: none in the current canonical recipe
Priority: communication overlap and router-path cleanup

Qwen3 235B on GB200

text

Dispatcher: HybridEP
TP=1 or 2  EP=32 to 64  PP=4  VPP=unspecified unless measured
CUDA Graph: attn + moe_router + moe_preprocess
Recompute: moe_act, mlp, or norm depending on memory pressure
Priority: balance throughput against memory headroom

Qwen3 30B-A3B on 16 H100

text

Dispatcher: HybridEP
TP=1  EP=16  PP=1  CP=1
Precision: BF16
Sequence: 4096
Batch: MBS1 GBS1024
Routing: force balance
EP overlap: enabled
Delayed wgrad: disabled
CUDA Graph: moe_router + moe_preprocess
HybridEP: permute fusion, 32 SMs, 64-token combine chunks
Measured: 20.14729s/step, 299.352 model TFLOPS/GPU over iterations 41-50
Rank-0 peak allocated memory: 62.166 GiB

The current number is the final multi-knob canonical recipe result. An earlier matched A/B isolated plain EP overlap: 244.039 to 287.305 TFLOPS/GPU, with communication hidden by GEMM/attention increasing from 0.11% to 36.55%. Do not attribute the later 299.352 result entirely to overlap.

Qwen3-Next 80B on GB200

text

Dispatcher: HybridEP
TP=1  EP=32  PP=2  VPP around 4
CUDA Graph: attn + moe_router + moe_preprocess
Priority: pipeline layout and grouped GEMM quality

Cross-Cutting Patterns

Show full SKILL.md (295 more words)Show less

PP layout

E = embedding
t = transformer
m = MTP
L = loss
| = stage boundary

The biggest platform difference is usually not just the dispatcher. It is the combination of dispatcher, PP shape, and whether VPP keeps each stage balanced.

Recompute strategy

Memory pressure	Starting point
low	none or a very narrow selective set
moderate	`moe_act`, `mlp`, `norm`, or similar selective modules
high	model-specific up-projection plus selective MoE and MLP modules
extreme or long-context	full recompute only if the selective path still does not fit

Environment variables

bash

CUDA_DEVICE_MAX_CONNECTIONS=1
CUDA_DEVICE_MAX_CONNECTIONS=32   # common when EP overlap and CUDA graphs are combined
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
NCCL_GRAPH_REGISTER=0

CPU-side tuning

On GB200 and GB300, CPU affinity and general host-overhead cleanup can move the needle almost as much as a dispatcher swap. Treat them as first-class tuning work, not as afterthoughts.

Pitfalls

Do not cargo-cult a tracker row: the winning config usually depends on routing mode, container, and PP layout as much as on hardware name.
Container quality matters: large regressions can come from the software stack rather than the model recipe.
VPP must be intentional: a bad VPP split can erase the gain from a better dispatcher.
Compare absolute throughput, not only MFU: MFU can mislead when switching between BF16, FP8, and other precision modes.
Force-balance routing is benchmark-only: it can control routing variance, but it changes semantics. Keep routing fixed within an A/B and validate natural routing separately for training acceptance.
Do not treat the dispatcher table as a hard platform rule: HybridEP is the validated winner for the canonical 16×H100 Qwen3 30B shape, while the current 256×H100 Qwen3 235B recipe uses alltoall. Benchmark backend compatibility and throughput in the production container.
Separate screening, causality, and acceptance: short runs reject weak candidates, matched one-variable A/Bs explain a mechanism, and a 50-step final run validates the complete winner.

Last signature refresh: 2026-08-03.

Files

SKILL.md and 5 other files in skills/nemo-mbridge-perf-moe-hardware-configs 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

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Nemo Mbridge Perf Moe Hardware Configs compared with similar skills
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Nemo Mbridge Perf Moe Hardware Configs this skillNVIDIA/skills	3.6k	—	~2k	Automated safety check: Pass	Apache-2.0	yesterday
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Code ReviewJakeATX/llamAmpere	157	—	~5.6k	Automated safety check: Pass	MIT	yesterday
Add Modelsohu-mptc/FlashRec	107	—	~1.7k	Automated safety check: Pass	Apache-2.0	today
AppJakeATX/llamAmpere	157	—	~146	Automated safety check: Pass	MIT	yesterday

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Works with

Questions about Nemo Mbridge Perf Moe Hardware Configs

What does Nemo Mbridge Perf Moe Hardware Configs do?

Representative, point-in-time MoE training playbooks by hardware and model family. Nemo Mbridge Perf Moe Hardware Configs is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Representative, point-in-time MoE training playbooks by hardware and model family.

When should I use Nemo Mbridge Perf Moe Hardware Configs?

Nemo Mbridge Perf Moe Hardware Configs fits situations like: AI & LLM Engineering work in your project.

How do I install Nemo Mbridge Perf Moe Hardware Configs in Claude Code?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-hardware-configs -a claude-code`. Or copy the skill folder (skills/nemo-mbridge-perf-moe-hardware-configs in NVIDIA/skills) into .claude/skills/nemo-mbridge-perf-moe-hardware-configs in your project. Claude Code loads it when a task matches its description.

How do I install Nemo Mbridge Perf Moe Hardware Configs in Codex?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-hardware-configs -a codex`. Or copy the skill folder (skills/nemo-mbridge-perf-moe-hardware-configs in NVIDIA/skills) into .agents/skills/nemo-mbridge-perf-moe-hardware-configs in your project. Codex loads it when a task matches its description.

Can I use Nemo Mbridge Perf Moe Hardware Configs 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-hardware-configs -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-hardware-configs, .gemini/skills/nemo-mbridge-perf-moe-hardware-configs, .github/skills/nemo-mbridge-perf-moe-hardware-configs and .opencode/skills/nemo-mbridge-perf-moe-hardware-configs in your project.

What does Nemo Mbridge Perf Moe Hardware Configs need to run?

SKILL.md names no scripts, command-line tools or credentials: Nemo Mbridge Perf Moe Hardware Configs is instructions for the agent only.

Does Nemo Mbridge Perf Moe Hardware Configs 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 Hardware Configs 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 Hardware Configs use?

Nemo Mbridge Perf Moe Hardware Configs 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 Hardware Configs use?

About 2k tokens (SKILL.md is roughly 7.9k 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 Hardware Configs?

Skills that share tags, products or a category with Nemo Mbridge Perf Moe Hardware Configs: Add New Model (JakeATX/llamAmpere, 157 stars), Debug Cuda Crash (sgl-project/sglang, 37k stars), Code Review (JakeATX/llamAmpere, 157 stars) and Add Model (sohu-mptc/FlashRec, 107 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 Hardware Configs?

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.

Install Nemo Mbridge Perf Moe Hardware Configs

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

MoE Hardware Configuration Reference

Quick Platform Playbook

First Answer Checklist

Rounded Performance Bands

Representative Config Families

DSV3 on H100

DSV3 on B200

DSV3 on GB200 or GB300

Qwen3 235B on H100

Qwen3 235B on GB200

Qwen3 30B-A3B on 16 H100

Qwen3-Next 80B on GB200

Cross-Cutting Patterns

PP layout

Recompute strategy

Environment variables

CPU-side tuning

Pitfalls

Files

Compare with similar skills

Similar skills

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Questions about Nemo Mbridge Perf Moe Hardware Configs