To optimize the inference efficiency of the Seed-OSS model, the following key aspects can be manipulated:
- Adjusting the thinking_budget parameter: This parameter is set dynamically (128-1024) according to the complexity of the task, with lower values set for simple tasks such as translation and higher values for complex mathematical reasoning.
- Parallel Computing with Multiple GPUs: By
tensor-parallel-sizeparameter (e.g., set to 8) allocates GPU resources to significantly increase throughput. - Choosing the right data type: Adoption
bfloat16Instead of float32, it maintains model accuracy and reduces the ~50% video memory footprint. - Deploying the vLLM Inference Framework: Its sequential batch technology increases throughput by a factor of 2-3, and is recommended to be installed via a pre-compiled version (
VLLM_USE_PRECOMPILED=1).
For continuous operation scenarios, it is recommended to establish a monitoring mechanism to dynamically adjust the above parameter combinations based on real-time load. For example, lowering the thinking_budget during low traffic periods and enabling more GPU nodes during peak periods.
This answer comes from the articleSeed-OSS: Open Source Large Language Model for Long Context Reasoning and Versatile ApplicationsThe
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