vllm - 💡(How to fix) Fix [Bug]: Gemma 4 26B MoE NVFP4 fails under tensor-parallel-size 2 across all available MoE backends [1 participants]

Official PRs (…)
ON THIS PAGE

Recommended Tools

×6

Utilities matched from this issue’s tags and category — try them while you read without losing context.

GitHub issue graph ai analysis

Paste a GitHub issue URL. We fetch that issue, discover linked issues from bodies/comments/timeline, collect linked pull requests, and produce a structured English report.

The report is written in English Markdown for sharing and archival.

Helpful · Quick feedback

Loading…
GitHub stats
vllm-project/vllm#39595Fetched 2026-04-12 13:24:30
View on GitHub
Comments
0
Participants
1
Timeline
1
Reactions
0
Author
Participants
Timeline (top)
labeled ×1
RAW_BUFFERClick to expand / collapse

Your current environment

<details> All community NVFP4 quantizations of `google/gemma-4-26B-A4B-it` fail to initialize under `--tensor-parallel-size 2`, each hitting a different unsupported kernel path. The official `nvidia/Gemma-4-31B-IT-NVFP4` works correctly under TP=2, confirming this is specific to the 26B MoE architecture's expert dimensions when split across two GPUs. </details>

🐛 Describe the bug

All community NVFP4 quantizations of google/gemma-4-26B-A4B-it fail to initialize under --tensor-parallel-size 2, each hitting a different unsupported kernel path. The official nvidia/Gemma-4-31B-IT-NVFP4 works correctly under TP=2, confirming this is specific to the 26B MoE architecture's expert dimensions when split across two GPUs.

Before submitting a new issue...

  • Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

extent analysis

TL;DR

The issue may be resolved by modifying the expert dimensions in the 26B MoE architecture to be compatible with tensor parallel size 2.

Guidance

  • Investigate the kernel paths that are causing the initialization failure to understand the specific compatibility issues with the 26B MoE architecture.
  • Compare the expert dimensions and tensor parallelization settings between the community NVFP4 quantizations of google/gemma-4-26B-A4B-it and the official nvidia/Gemma-4-31B-IT-NVFP4 to identify potential differences.
  • Consider testing with a smaller tensor parallel size or modifying the model architecture to accommodate the parallel size 2.
  • Verify that the issue is indeed specific to the 26B MoE architecture and not a more general problem with tensor parallelization.

Notes

The solution may require significant modifications to the model architecture or the tensor parallelization settings, and may not be compatible with all use cases.

Recommendation

Apply workaround: Modify the expert dimensions in the 26B MoE architecture to be compatible with tensor parallel size 2, as the official nvidia/Gemma-4-31B-IT-NVFP4 model works correctly under TP=2.

Vote matrix · Quick signals

Works
Did the solution work? Tap to confirm.
Easy Fix
Was it a quick fix?
Time Saver
Did it save you time?
Blocking
Was it severely blocking?
Common Issue
Are others likely hitting this too?
Flaky / Intermittent
Is it intermittent?
Verified / Reproducible
Can you reproduce it reliably?
Loading…

Still need to ship something?

×6

Another batch ranked right after the header list — different links, same matching logic.

Back to top recommendations

TRENDING