Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA-NeMo/Megatron-Bridge --skill nemo-mbridge-perf-parallelism-strategiesgit clone --depth 1 https://github.com/NVIDIA-NeMo/Megatron-BridgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-parallelism-strategies)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-parallelism-strategies"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-parallelism-strategies/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-parallelism-strategies"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-parallelism-strategies.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 267 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.02892 |
| Opus 5 | $0.00020 | $0.01446 |
| Sonnet 5 | $0.00008 | $0.00578 |
| Haiku 4.5 | $0.00004 | $0.00289 |
Grade A, and why
nemo-mbridge-perf-parallelism-strategies scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 | 64-256 | TP=8 + PP=4-8 + DP |
| 175-500B | 256-1024 | TP=8 + PP=8-16 + CP=2 + DP |
MoE models
MoE parallelism differs from dense models. Because only a fraction of parameters are active per token, TP can often stay at 1 or 2 — the active parameter shard already fits on a single GPU. EP is the primary scaling dimension, with PP handling cross-node layer distribution.
| Model (total / active) | TP | PP | EP | Notes |
|---|---|---|---|---|
| OLMoE 7B / 1B | 1 | 1 | 8 | EP only, fits single node |
| Moonlight 16B / 3B | 2 | 1 | 8 | small TP for shared layers |
| DeepSeek-V2 236B / 21B | 1 | 4 | 32 | no TP at all |
| GLM-4.5 Air 106B / 12B | 1 | 4 | 8 | no TP at all |
| Qwen3 30B-A3B | 4 | 2 | 4 | |
| GLM-4.5 355B / 32B | 2 | 8 | 16 | |
| Qwen3 235B-A22B | 4 | 16 | 8 | CP=2 for pretrain |
| DeepSeek-V3 671B / 37B | 2 | 16 | 64 | TP=2, not 8 |
| Kimi-K2 1T | 2 | 16 | 32 |
Key patterns:
- TP is sized by active params, not total params. A 671B MoE with 37B active needs far less TP than a 70B dense model.
- EP scales with expert count. Common: EP = num_experts or num_experts / experts_per_gpu.
- PP handles depth. Large MoE models use PP=8-16 across nodes.
- ETP (expert tensor parallelism) is rarely used. Llama 4 is an exception (ETP=4).
These are starting points, not hard rules. Always profile the first iteration to verify memory and communication.
Decision by Hardware Topology
Single node with NVLink:
cfg.model.tensor_model_parallel_size = 8
Multiple nodes with InfiniBand:
cfg.model.tensor_model_parallel_size = 8
cfg.model.pipeline_model_parallel_size = N
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 298 lines · 41 tokens per session scan A 3036c4f17732
nemo-mbridge-perf-parallelism-strategies is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (905 stars, last pushed yesterday), licensed Apache-2.0. It adds 41 tokens to every session and 2,892 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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