Skip to content

[https://nvbugs/6094208][fix] AutoDeploy: skip bf16 Nemotron-Nano-V3 accuracy test on <80GB GPUs - #14243

Merged
galagam merged 1 commit into
NVIDIA:mainfrom
nv-auto-deploy:gagam/nvbug-6094208
May 18, 2026
Merged

[https://nvbugs/6094208][fix] AutoDeploy: skip bf16 Nemotron-Nano-V3 accuracy test on <80GB GPUs#14243
galagam merged 1 commit into
NVIDIA:mainfrom
nv-auto-deploy:gagam/nvbug-6094208

Conversation

@galagam

@galagam galagam commented May 18, 2026

Copy link
Copy Markdown
Collaborator

Summary by CodeRabbit

  • Tests
    • Updated test configuration to improve resource handling for specific model variants.

Review Change Stack

Description

The Nano 30B bf16 weights alone nearly exceed the memory available on L40-class cards, so the test OOMs in CI even with low-memory overrides applied. Gate the bf16 parametrize entry on >=80GB device memory; fp8/nvfp4 remain gated by their existing architecture markers.

Test Coverage

PR Checklist

Please review the following before submitting your PR:

  • PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

  • PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

  • Test cases are provided for new code paths (see test instructions)

  • If PR introduces API changes, an appropriate PR label is added - either api-compatible or api-breaking. For api-breaking, include BREAKING in the PR title.

  • Any new dependencies have been scanned for license and vulnerabilities

  • CODEOWNERS updated if ownership changes

  • Documentation updated as needed

  • Update tava architecture diagram if there is a significant design change in PR.

  • The reviewers assigned automatically/manually are appropriate for the PR.

  • Please check this after reviewing the above items as appropriate for this PR.

GitHub Bot Help

To see a list of available CI bot commands, please comment /bot help.

…accuracy test on <80GB GPUs

The Nano 30B bf16 weights alone nearly exceed the memory available on
L40-class cards, so the test OOMs in CI even with low-memory overrides applied.
Gate the bf16 parametrize entry on >=80GB device memory; fp8/nvfp4
remain gated by their existing architecture markers.

Signed-off-by: Gal Hubara Agam <96368689+galagam@users.noreply.github.com>
@galagam
galagam requested review from a team as code owners May 18, 2026 07:41
@galagam
galagam requested a review from greg-kwasniewski1 May 18, 2026 07:41
@coderabbitai

coderabbitai Bot commented May 18, 2026

Copy link
Copy Markdown
Contributor

No actionable comments were generated in the recent review. 🎉

ℹ️ Recent review info
⚙️ Run configuration

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

Plan: Enterprise

Run ID: 05e99335-c56a-44a3-a870-f4980cb830e2

📥 Commits

Reviewing files that changed from the base of the PR and between bd54511 and 3c28a87.

📒 Files selected for processing (1)
  • tests/integration/defs/accuracy/test_llm_api_autodeploy.py

📝 Walkthrough

Walkthrough

A single test parameter in the Nemotron accuracy test is updated to include a conditional skip mark. The "bf16" model variant now skips execution on machines with insufficient device memory (less than 80000), replacing an unmarked plain parameter entry.

Changes

Test skip condition for Nemotron bf16 model

Layer / File(s) Summary
Nemotron bf16 model test skip mark
tests/integration/defs/accuracy/test_llm_api_autodeploy.py
The "bf16" entry in TestNemotronNanoV3.test_accuracy parametrization is wrapped in pytest.param with skip_less_device_memory(80000) to conditionally skip on machines with less than 80000 device memory.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

Possibly related PRs

  • NVIDIA/TensorRT-LLM#13846: Also modifies test skip conditions and parametrization logic in the same AutoDeploy accuracy test file based on device memory constraints.

Suggested reviewers

  • lucaslie
  • MrGeva
🚥 Pre-merge checks | ✅ 5
✅ Passed checks (5 passed)
Check name Status Explanation
Title check ✅ Passed The title clearly and specifically describes the main change: adding a memory threshold skip condition for the bf16 Nemotron-Nano-V3 accuracy test. It references the bug ID, type, and the concrete condition (<80GB GPUs).
Description check ✅ Passed The PR description clearly explains the issue (Nano 30B bf16 weights cause OOM on L40-class cards) and the solution (gate test on >=80GB device memory). However, the Test Coverage section is incomplete with placeholder comment only.
Docstring Coverage ✅ Passed No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check ✅ Passed Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check ✅ Passed Check skipped because no linked issues were found for this pull request.

✏️ Tip: You can configure your own custom pre-merge checks in the settings.

✨ Finishing Touches
🧪 Generate unit tests (beta)
  • Create PR with unit tests

Comment @coderabbitai help to get the list of available commands and usage tips.

@galagam
galagam requested a review from MrGeva May 18, 2026 07:47
@galagam

galagam commented May 18, 2026

Copy link
Copy Markdown
Collaborator Author

/bot run

@tensorrt-cicd

Copy link
Copy Markdown
Collaborator

PR_Github #48885 [ run ] triggered by Bot. Commit: 3c28a87 Link to invocation

@tensorrt-cicd

Copy link
Copy Markdown
Collaborator

PR_Github #48885 [ run ] completed with state SUCCESS. Commit: 3c28a87
/LLM/main/L0_MergeRequest_PR pipeline #38635 completed with status: 'SUCCESS'

CI Report

Link to invocation

@galagam
galagam merged commit 4abc1fd into NVIDIA:main May 18, 2026
11 checks passed
KleinBlueC pushed a commit to KleinBlueC/TensorRT-LLM that referenced this pull request May 19, 2026
…accuracy test on <80GB GPUs (NVIDIA#14243)

Signed-off-by: Gal Hubara Agam <96368689+galagam@users.noreply.github.com>
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

None yet

Projects

None yet

Development

Successfully merging this pull request may close these issues.

3 participants