Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/NVIDIA-NeMo/Megatron-Bridgenpx agentmods add skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-expert-parallel-overlapWrote 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-expert-parallel-overlap)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-expert-parallel-overlap"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-expert-parallel-overlap/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-expert-parallel-overlap"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-expert-parallel-overlap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.03590 |
| Opus 5 | $0.00028 | $0.01795 |
| Sonnet 5 | $0.00011 | $0.00718 |
| Haiku 4.5 | $0.00006 | $0.00359 |
Grade A, and why
nemo-mbridge-perf-expert-parallel-overlap 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.
This is a copy
100% identical to nemo-mbridge-perf-expert-parallel-overlap — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MoE Expert-Parallel Overlap Skill
References
- Stable docs: @docs/training/communication-overlap.md
- Structured metadata: @skills/nemo-mbridge-perf-expert-parallel-overlap/card.yaml
What It Is
Expert-parallel (EP) overlap hides the cost of token dispatch/combine all-to-all
communication by running it concurrently with expert FFN compute. Optionally,
delayed expert weight-gradient computation (delay_wgrad_compute) provides
additional overlap by deferring wgrad to overlap with the next layer's forward.
Bridge supports two dispatcher paths:
| Dispatcher | Backend | When to use |
|---|---|---|
alltoall |
Standard MoE all-to-all | Default, broadest compatibility |
flex |
DeepEP or HybridEP | Higher overlap on Ampere/Hopper/Blackwell |
Quick Decision
Use EP overlap when:
- the model is MoE with
EP > 1 - expert dispatch/combine communication is a meaningful part of step time
- you have memory headroom and are tuning for throughput
Prefer:
alltoalldispatcher for the first rollout (broader compatibility)flex+ DeepEP/HybridEP when running on supported GPUs and seeking additional gains
Avoid EP overlap when:
- full activation recompute is enabled
moe_shared_expert_overlapis enabled- the run is still being brought up for correctness
- PyTorch < 2.6.0
Expected outcome:
- if all-to-all dispatch is a clear profile bottleneck, overlap can produce a modest to meaningful speedup
- if the run is tiny, communication-light, or dominated by another wall, the gain may be negligible
Correctness-First alltoall Benchmark
For the plain EP-overlap isolation benchmark, keep flex dispatch and delayed
wgrad disabled. The measured shape was Qwen3 MoE 30B-A3B SFT on 16 H100 GPUs:
EP=16, alltoall, BF16, global batch size 1024, CUDA graphs disabled,
moe_permute_fusion=false, measured over iterations 3-8.
Use these overrides for the plain-overlap case:
--cuda_graph_impl none \
--moe_flex_dispatcher_backend None \
--moe_a2a_overlap false \
comm_overlap.overlap_moe_expert_parallel_comm=true \
comm_overlap.delay_wgrad_compute=false \
model.moe_shared_expert_overlap=false
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 · 362 lines · 56 tokens per session scan A c8836f513ccd
nemo-mbridge-perf-expert-parallel-overlap is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (906 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 3,590 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to nemo-mbridge-perf-expert-parallel-overlap, differing in 0 lines, and is treated as a copy.
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