[Usage]: Optimal Config for NVFP4 - Qwen3 Next
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General perf
question
- Dominant language
- Python
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Description
System Info
Serving on B200 and getting mediocre results with the stock config
max_batch_size: 16
max_num_tokens: 4096
tensor_parallel_size: 4
moe_expert_parallel_size: 4
trust_remote_code: true
enable_attention_dp: false
cuda_graph_config:
enable_padding: true
max_batch_size: 720
moe_config:
backend: TRTLLM
stream_interval: 20
num_postprocess_workers: 4
kv_cache_config:
enable_block_reuse: false
free_gpu_memory_fraction: 0.6
are there any weird ways to improve nvfp4 performance hre?
How would you like to use TensorRT-LLM
serving qwen3Next in fp4
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- Read the whole issue, then the project's contributing guide.
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- Fork the repository and make your change on a branch.
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Research direction
Use the supplied serving configuration as the baseline and consult the TensorRT-LLM documentation and examples for Qwen3 Next and NVFP4. Measure B200 serving performance after each configuration change; done means identifying a configuration that improves the reported results and documenting the settings used.
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Assessment
- Tech stack
- python
- Domain
- ai, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100