Resource optimization solutions for efficient deployment
For hardware optimization for MiMo-7B model deployment, solutions can be implemented in the following three dimensions:
1. Inference engine selection
- vLLM engine: Xiaomi's customized version increases the A100 GPU's video memory utilization by 65% through PagedAttention technology, supporting simultaneous processing of 4-6 concurrent requests
- SGLang Program: Ideal for edge device deployments, with a memory footprint of 28GB or less in CPU mode
2. Precise configuration of parameters
- Batch resizing:
python3 -m vllm.entrypoints.api_server --model XiaomiMiMo/MiMo-7B-RL --max_num_seqs 4 - Enable FP16 quantization:
from_pretrained(model_id, torch_dtype=torch.float16) - Limit the context length:
SamplingParams(max_tokens=512)
3. Resilient deployment strategy
Recommended configurations for different scenarios:
| take | configure | Depletion of resources |
|---|---|---|
| development testing | Hugging Face + CPU | 32GB RAM |
| production environment | vLLM + A100 | 1×GPU |
| edge computing | SGLang + T4 | 16GB video memory |
Special Tip:
1. Utilizationnvidia-smiMonitor GPU utilization, recommended to keep it at 70%-80% Load
2. For mathematical reasoning tasks, you can turn off the logprob calculation to improve throughput.
3. Regular callstorch.cuda.empty_cache()Releasing the cache
With the above scenario, a typical deployment saves 42% in hardware resource consumption.
This answer comes from the articleMiMo: A Small Open Source Model for Efficient Mathematical Reasoning and Code GenerationThe
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