Agent skill

nemo-mbridge-perf-parallelism-strategies

Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.

NVIDIAgithub.com/NVIDIAGitHub ↗
claude-codecodexApache-2.0
Install
npx skills add NVIDIA/skills --skill nemo-mbridge-perf-parallelism-strategies --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 6
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/nemo-mbridge-perf-parallelism-strategies/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,789
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Parallelism Strategy Selection Skill For stable background on each parallelism type, see: - @docs/parallelisms.md - @skills/nemo-mbridge-perf-parallelism-strategies/card.yaml ## Decision by Model Size ### Dense models | Model size | GPUs | Recommended starting point | |---|---|---| | < 1B | 1-8 | DP only | | 1-10B | 8-16 | TP=2-4 + DP | | 10-70B | 16-64 | TP=4-8 + PP=2-4 + DP | | 70-175B | 64-25

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About this skill
What does the nemo-mbridge-perf-parallelism-strategies skill do?

Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.

How do I install it?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-parallelism-strategies --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.

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