Menu

Individual skill

nemo-mbridge-perf-moe-long-context

0upvotes

Long-context MoE training guidance for Megatron Bridge. Covers CP sizing, selective recompute, dispatcher choices, and practical patterns from DSV3, Qwen3, and Qwen3-Next long-context experiments.

skills/nemo-mbridge-perf-moe-long-context/SKILL.md

Read pinned source

Source commit fce3cca4170be75e82c64920654d56ee2811b005

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Instructions only
Last checked
Nov 9, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
3,906
Repository last updated
Nov 6, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# MoE Long-Context Training

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

## What Changes At Long Context

Once sequence length moves well past the 4K-class regime, attention memory and
activation residency become the dominant constraints. For MoE models, that
usually means you need some combination of:

- context parallelism
- selective recompute
- lower precision