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-long-contextgit 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-long-context)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-moe-long-context"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-moe-long-context/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-long-context"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-mbridge-perf-moe-long-context.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 121 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 | $0.00055 | $0.01258 |
| Opus 5 | $0.00028 | $0.00629 |
| Sonnet 5 | $0.00011 | $0.00252 |
| Haiku 4.5 | $0.00006 | $0.00126 |
Grade A, and why
nemo-mbridge-perf-moe-long-context 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 11d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- CPU offload for optimizer state
- a dispatcher and PP layout that do not waste the smaller remaining DP budget
Rounded Scaling Patterns
DSV3 on H100
The DSV3 long-context runs show a stable pattern:
- selective recompute works better than full recompute once you move past the shortest contexts
- throughput stays in a fairly narrow band from mid-length through very long contexts if CP is increased appropriately
- the trade shifts from "memory fit" to "GPU-count feasibility" as CP grows
In other words, long context does not immediately collapse utilization if the layout is chosen well, but it does consume the DP budget very quickly.
Qwen3-Next on GB200
Qwen3-Next behaves more like a memory-sensitive medium-scale model:
- 8K and 32K remain practical with moderate CP
- 64K is possible, but the throughput drop is noticeable and memory becomes much tighter
- pipeline layout and grouped-GEMM improvements matter almost as much as CP
Qwen3 235B on GB200
Qwen3 235B shows that long context can still be efficient on NVL72 systems when TP, CP, and HybridEP are coordinated. The best 128K-class configurations are not just "fit-only" recipes; they can remain highly efficient if routing, parallelism, and recompute are balanced.
CP Sizing Rules Of Thumb
-
Start from a 4K shard target: a good first guess is
CP ~= seq_len / 4096, then round to a practical power-of-two layout. -
Keep DP alive if possible: long-context scaling becomes brittle once CP, EP, TP, and PP together squeeze DP down to the floor.
-
Prefer selective recompute: recompute modules such as
up_proj,norm,moe,moe_act, ormlpbefore reaching for full recompute.
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.
- 11d ago First seen · 140 lines · 55 tokens per session scan A 5fef955b4395
nemo-mbridge-perf-moe-long-context is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (905 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 1,258 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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