TensorRT-LLM 0.17.0.post1 fails to run Whisper on 5080 GPU
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 14.7k
- Forks
- 2.8k
- Avg merge
- 2d 23h
- Merged PRs (30d)
- 489
Description
System Info
- GPU 5080
- TensorRT-LLM: 0.17.0.post1
- PyTorch: 2.7.0.dev20250302+cu128
- CUDA: 12.8
Who can help?
No response
Information
- The official example scripts
- My own modified scripts
Tasks
- An officially supported task in the
examplesfolder (such as GLUE/SQuAD, ...) - My own task or dataset (give details below)
Reproduction
tensorrt-llm 0.17.0.post1 requires torch<=2.6.0a0,>=2.5.1, but you have torch 2.7.0.dev20250302+cu128 which is incompatible.
Expected behavior
pass
actual behavior
torchaudio 2.6.0.dev20250302+cu128 requires torch==2.7.0.dev20250302, but you have torch 2.5.1 which is incompatible.
additional notes
Description:
When trying to run Whisper on an NVIDIA 5080 GPU using TensorRT-LLM 0.17.0.post1, the process fails. The issue may be related to compatibility with Torch 2.7.0.dev20250302+cu128 or specific support for the 5080 GPU in TensorRT-LLM.
Steps to Reproduce:
- Install the following environment on a system with 5080 GPU:
- TensorRT-LLM: 0.17.0.post1
- PyTorch: 2.7.0.dev20250302+cu128
- CUDA: 12.8
- Attempt to accelerate Whisper using TensorRT-LLM.
- The execution fails with an error message (see below).
Expected Behavior:
Whisper should run successfully on the 5080 GPU with TensorRT-LLM acceleration.
Actual Behavior:
The execution fails, producing the following error (please provide the full error log):
tensorrt-llm 0.17.0.post1 requires torch<=2.6.0a0,>=2.5.1, but you have torch 2.7.0.dev20250302+cu128 which is incompatible.
System Information:
- GPU: NVIDIA 5080
- CUDA Version: 12.8
- TensorRT-LLM Version: 0.17.0.post1
- PyTorch Version: 2.7.0.dev20250302+cu128
- Whisper Model Version: (Please specify the exact model version)
Questions & Suggestions:
- Is there any plan to support Torch 2.7.0.dev20250302+cu128?
- Is there a workaround or patch available for 5080 GPU compatibility?
Thank you for your support! Looking forward to any guidance or a potential fix.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the reported environment with NVIDIA 5080, CUDA 12.8, TensorRT-LLM 0.17.0.post1, PyTorch 2.7.0.dev20250302+cu128, and torchaudio, then inspect the dependency conflict and Whisper support. Done means identifying a supported compatible combination or documenting the specific limitation and workaround; the issue does not name files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100