Model Service Performance Optimization Solution
The inference performance of open source models such as Qwen3-8B-CK-Pro can be significantly improved with the following configurations:
- parallel processing: Set when starting vLLM service
--tensor-parallel-size 8Leverage multiple GPUs - Memory Optimization: Adjustments
--max-model-len 8192Control maximum context length - hardware adaptation: Adjusted for video memory size
--worker-use-rayThe number of workers in the - Service Monitoring: By
nvidia-smiMonitor GPU utilization and dynamically adjust concurrent requests
It is recommended to execute the model server before it startsexport NCCL_IB_DISABLE=1Some network communication problems can be avoided. Measurements have shown that a reasonable configuration can enable the 8B model to reach a generation rate of 30+ tokens per second on an A100 graphics card.
This answer comes from the articleCognitive Kernel-Pro: a framework for building open source deep research intelligencesThe
May not be reproduced without permission:AI productivity tools " How to optimize the performance of Cognitive Kernel-Pro's model service?
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