For MiMo-7B inference deployment, Xiaomi's deeply optimized vLLM branch (based on vLLM 0.7.3) shows significant performance advantages. This customized version fully supports MTP technology and can achieve a stable throughput of 20+ tokens per second on hardware such as NVIDIA A100. Technical comparisons show that compared to the native Hugging Face Transformers interface, the customized vLLM improves memory utilization by 351 TP3T and reduces inference latency by 401 TP3T.
For deployment scenarios, it is recommended to usepython3 -m vllm.entrypoints.api_serverLaunch services for highly concurrent access via REST API. The system requires a single GPU (e.g., A100 40GB) to run smoothly and supports thetemperature=0.6The parameter settings balance generation quality with variety. SGLang can also be chosen as a lightweight alternative for scenarios that require rapid prototyping.
This answer comes from the articleMiMo: A Small Open Source Model for Efficient Mathematical Reasoning and Code GenerationThe
