Background
Primary care organizations often face the dilemma of limited budgets and insufficient technological power, and need to balance model performance with deployment costs. the 4-bit quantization feature of Baichuan-M2-32B provides a breakthrough in this dilemma.
Core Programs
- Hardware Selection Strategy::
Using NVIDIA RTX 4090+Intel i7 combination, RAM recommended 32GB or more, total cost can be controlled within 30,000 RMB - Mixed-precision inference::
Combined use of torch.bfloat16 (non-critical layer) + 4-bit quantization (large parameter layer) in transformers calls reduces 30% video memory usage - Service-oriented deployment::
Using vLLM's sequential batch processing feature, a single instance can handle 5-8 interrogation requests simultaneously, significantly improving hardware utilization
advanced skill
1. fine-tune adaptation of local common diseases via LoRA 2. set max_new_tokens=1024 to limit the generation length 3. enable request priority scheduling for sglang to ensure priority response for urgent problems
This answer comes from the articleBaichuan-M2: A Large Language Model for Augmented Reasoning in HealthcareThe
May not be reproduced without permission:AI productivity tools " How to optimize the cost of deploying Baichuan-M2-32B in primary care hospitals?
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