[AutoDeploy]: Optimize the performance of gpt-oss (orangina)
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AutoDeploy
- Dominant language
- Python
- Stars
- 14.7k
- Forks
- 2.8k
- Avg merge
- 2d 23h
- Merged PRs (30d)
- 489
Description
🚀 The feature, motivation and pitch
- FP8, FP4
- H200, B200
- Large ISL/OSL
- See https://confluence.nvidia.com/pages/viewpage.action?spaceKey=GCA&title=2025-07+CLU
Alternatives
No response
Additional context
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Research direction
No source file, test, or entry point is identified. Start by reviewing the TensorRT-LLM documentation and examples linked in the issue, then locate the AutoDeploy implementation and define measurable performance goals for FP8/FP4, H200/B200, and large ISL/OSL workloads.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- backend, machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100