Generated banner for Nemo Mbridge Perf Moe Hardware Configs
Generated logo for Nemo Mbridge Perf Moe Hardware Configs

Nemo Mbridge Perf Moe Hardware Configs

NVIDIAOfficialPublished by NVIDIA

  • Skill
  • Guidelines
  • Data and AI
  • Apache-2.0
  • Medium risk

From the author

Representative, point-in-time MoE training playbooks by hardware and model family. Use them as candidate seeds, then revalidate the exact runtime, semantics, topology, and steady-state throughput.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured reference for Mixture-of-Experts (MoE) training configurations. It outlines representative playbooks, performance bands, and tuning strategies for different hardware (H100, B200, GB200, GB300) and model families (DSV3, Qwen3). It emphasizes that these configurations serve as candidate seeds for performance optimization rather than absolute defaults, highlighting the importance of revalidation through profile-driven testing, dispatcher selection, and environment tuning.

What it does

  • Provides candidate hardware-specific MoE training configurations.
  • Defines performance bands and representative shapes for planning.
  • Lists critical environment variables for performance tuning.
  • Outlines recompute strategies based on memory pressure.
  • Details CPU-side tuning requirements for GB200/GB300 systems.
  • Identifies common pitfalls in MoE performance benchmarking.

When to use it

  • When planning MoE training runs on H100, B200, or GB200/300 hardware.
  • When seeking candidate seeds for model parallelism and dispatcher settings.
  • When troubleshooting performance regressions in MoE training pipelines.
  • When establishing a baseline for throughput and memory usage.

Advantages

  • Reduces trial-and-error by providing validated starting configurations.
  • Encourages rigorous performance validation through A/B testing.
  • Highlights the impact of software stack and container quality on throughput.
  • Provides clear guidance on balancing memory headroom and throughput.

Usage examples

“What is a recommended configuration for training DSV3 on H100 hardware?”
The agent provides the DeepEP dispatcher configuration with specific TP, EP, PP, and VPP settings, noting the importance of communication overlap.
“How should I tune GB200 systems for MoE training?”
The agent suggests using HybridEP, focusing on CPU-side tuning, graph-friendly static shapes, and specific environment variables like NCCL_GRAPH_REGISTER=0.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/nemo-mbridge-perf-moe-hardware-configs/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip nemo-mbridge-perf-moe-hardware-configs.zip -d .agents/skills

Documentation by NVIDIA

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.

PlatformCandidates to screen after alltoall bring-upWhat usually matters most
H100DeepEP or HybridEP, explicit overlap, supported FP8 modescommunication overlap, dispatcher/runtime compatibility, and PP efficiency
B200DeepEP or HybridEP, supported FP8 modes, careful PP layoutcontainer quality and tuned communication settings
GB200HybridEP, then profile-driven graphs and CPU cleanuphost overhead, topology-aware dispatch, memory headroom
GB300HybridEP and the target container's lower-precision/kernel stackthe same system interactions as GB200, with remeasurement required

First Answer Checklist

For hardware playbook questions, answer from these canonical rows before adding throughput caveats:

WorkloadHardwareDispatcherLayout
DSV3H100DeepEPTP=2, EP=64, PP=8, VPP=4
DSV3GB200/GB300HybridEPTP=1, EP=64, PP=4, VPP=4
Qwen3 235BH100alltoall + overlap in the current canonical recipeTP=2, EP=32, PP=8, VPP=4
Qwen3 235BGB200HybridEPTP=1 or 2, EP=32-64, PP=4, VPP=unspecified
Qwen3 30B16×H100HybridEPTP=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.

Workload familyHardwareTypical bandRepresentative shape
DSV3, large-scaleH100low-to-mid hundreds TFLOPS/GPU, high-teens MFUTP2, EP64, PP8, DeepEP
DSV3, large-scaleB200high-hundreds TFLOPS/GPU, mid-teens MFUTP1, EP32, PP8, DeepEP
DSV3, large-scaleGB200around 1K TFLOPS/GPU, low-20s MFUTP1, EP64, PP4, HybridEP
DSV3, large-scaleGB300above the GB200 band, often mid-20s MFUTP1, EP64, PP4, HybridEP
Qwen3 235BH100historical low-300s snapshots; remeasure the current recipeTP2, EP32, PP8; current recipe uses alltoall + overlap
Qwen3 235BGB200high-hundreds TFLOPS/GPU in tuned runsTP1 or TP2, EP32-64, PP4, HybridEP
Qwen3 30BH100about 300 TFLOPS/GPU on the validated 16-GPU shapeTP1, EP16, PP1, HybridEP + EP overlap
Qwen3-Next 80BGB200low-300s TFLOPS/GPU in BF16-class runsTP1, EP32, PP2, HybridEP

Representative Config Families

DSV3 on H100
Dispatcher: DeepEP
TP=2  EP=64  PP=8  VPP=4
Routing: force balance
Recompute: light-to-moderate selective recompute
Priority: overlap communication and keep PP efficient
DSV3 on B200
Dispatcher: DeepEP
TP=1  EP=32  PP=8  VPP=2 or similar
Precision: MXFP8-class
Recompute: selective recompute around MLA up-projection and MLP-side modules
Priority: container quality, PP layout, and DeepEP SMS tuning
DSV3 on GB200 or GB300
Dispatcher: HybridEP
TP=1  EP=64  PP=4  VPP=4
Precision: MXFP8-class
CUDA Graph: attn + moe_router + moe_preprocess
Priority: HybridEP, CPU optimization, and graph-friendly static shapes
Qwen3 235B on H100
Dispatcher: alltoall in the current canonical recipe; re-screen flex backends on the target stack
TP=2  EP=32  PP=8  VPP=4
Recompute: none in the current canonical recipe
Priority: communication overlap and router-path cleanup
Qwen3 235B on GB200
Dispatcher: HybridEP
TP=1 or 2  EP=32 to 64  PP=4  VPP=unspecified unless measured
CUDA Graph: attn + moe_router + moe_preprocess
Recompute: moe_act, mlp, or norm depending on memory pressure
Priority: balance throughput against memory headroom
Qwen3 30B-A3B on 16 H100
Dispatcher: HybridEP
TP=1  EP=16  PP=1  CP=1
Precision: BF16
Sequence: 4096
Batch: MBS1 GBS1024
Routing: force balance
EP overlap: enabled
Delayed wgrad: disabled
CUDA Graph: moe_router + moe_preprocess
HybridEP: permute fusion, 32 SMs, 64-token combine chunks
Measured: 20.14729s/step, 299.352 model TFLOPS/GPU over iterations 41-50
Rank-0 peak allocated memory: 62.166 GiB

The current number is the final multi-knob canonical recipe result. An earlier matched A/B isolated plain EP overlap: 244.039 to 287.305 TFLOPS/GPU, with communication hidden by GEMM/attention increasing from 0.11% to 36.55%. Do not attribute the later 299.352 result entirely to overlap.

Qwen3-Next 80B on GB200
Dispatcher: HybridEP
TP=1  EP=32  PP=2  VPP around 4
CUDA Graph: attn + moe_router + moe_preprocess
Priority: pipeline layout and grouped GEMM quality

Cross-Cutting Patterns

PP layout
  • E = embedding

  • t = transformer

  • m = MTP

  • L = loss

  • | = stage boundary

The biggest platform difference is usually not just the dispatcher. It is the combination of dispatcher, PP shape, and whether VPP keeps each stage balanced.

Recompute strategy
Memory pressureStarting point
lownone or a very narrow selective set
moderatemoe_act, mlp, norm, or similar selective modules
highmodel-specific up-projection plus selective MoE and MLP modules
extreme or long-contextfull recompute only if the selective path still does not fit
Environment variables
CUDA_DEVICE_MAX_CONNECTIONS=1
CUDA_DEVICE_MAX_CONNECTIONS=32   # common when EP overlap and CUDA graphs are combined
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
NCCL_GRAPH_REGISTER=0
CPU-side tuning

On GB200 and GB300, CPU affinity and general host-overhead cleanup can move the needle almost as much as a dispatcher swap. Treat them as first-class tuning work, not as afterthoughts.

Pitfalls

  1. Do not cargo-cult a tracker row: the winning config usually depends on routing mode, container, and PP layout as much as on hardware name.

  2. Container quality matters: large regressions can come from the software stack rather than the model recipe.

  3. VPP must be intentional: a bad VPP split can erase the gain from a better dispatcher.

  4. Compare absolute throughput, not only MFU: MFU can mislead when switching between BF16, FP8, and other precision modes.

  5. Force-balance routing is benchmark-only: it can control routing variance, but it changes semantics. Keep routing fixed within an A/B and validate natural routing separately for training acceptance.

  6. Do not treat the dispatcher table as a hard platform rule: HybridEP is the validated winner for the canonical 16×H100 Qwen3 30B shape, while the current 256×H100 Qwen3 235B recipe uses alltoall. Benchmark backend compatibility and throughput in the production container.

  7. Separate screening, causality, and acceptance: short runs reject weak candidates, matched one-variable A/Bs explain a mechanism, and a 50-step final run validates the complete winner.

Last signature refresh: 2026-08-03.

Files

LICENSE

Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
NVIDIA
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/nemo-mbridge-perf-moe-hardware-configs · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nemo-mbridge-perf-moe-hardware-configs.zip