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-moe-hardware-configsgit 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-moe-hardware-configs)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-moe-hardware-configs"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-moe-hardware-configs/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-moe-hardware-configs"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-moe-hardware-configs.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 | $0.00051 | $0.02244 |
| Opus 5 | $0.00026 | $0.01122 |
| Sonnet 5 | $0.00010 | $0.00449 |
| Haiku 4.5 | $0.00005 | $0.00224 |
Grade A, and why
nemo-mbridge-perf-moe-hardware-configs 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 — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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 · 208 lines · 51 tokens per session scan A c1382d1cbec1
nemo-mbridge-perf-moe-hardware-configs is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (905 stars, last pushed yesterday), licensed Apache-2.0. It adds 51 tokens to every session and 2,244 once invoked, about $0.0003 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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