Graphics Memory Optimization
For large models such as the Qwen 2.5-32B, the display memory problem:
- Core Programs::
- Activating DeepSpeed's ZeRO-3 optimization: in the
deepspeed_config.jsonset up in"stage": 3 - Memory Pool Management with vLLM: Add
--use-vllmpriming parameter - Enabling 8-bit quantization: configuration
--load-in-8bitReduces 60% video memory footprint
- Activating DeepSpeed's ZeRO-3 optimization: in the
- Options::
- Gradient accumulation technique: setting
--gradient-accumulation-steps 8 - Model slicing: by
--device-map autoAutomatic allocation of multi-GPU video memory
- Gradient accumulation technique: setting
Hardware Adaptation Recommendations
Selected based on model size:
- Qwen 2.5-7B: Minimum 1 x A10G (24GB) required
- Qwen2.5-32B: 4 x A100 (80GB) configuration recommended
- For consumer graphics cards: modifiable
modeling_qwen.pyAttention_head_dim reduces the head dimension in the
This answer comes from the articleOpen-Reasoner-Zero: Open Source Large-Scale Reasoning Reinforcement Learning Training PlatformThe
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