Hardware requirements and performance optimization
Basic Hardware Requirements
- GPUsNVIDIA GPUs with at least 8GB of video memory are recommended.
- random access memory (RAM): 16GB or more of system memory recommended
- stockpile: Need enough space to store training datasets (COCO, etc.)
Recommendations for optimization of the training phase
- Multi-GPU Parallelism: Utilizing multi-GPU acceleration with the -nproc_per_node parameter
- Batch size adjustment: adjust per_device_train_batch_size according to video memory size
- gradient accumulation: Simulate larger batches using gradient_accumulation_steps
- Mixed precision training: Enable bf16 or fp16 to reduce video memory usage
Reasoning phase optimization recommendations
- Flash Attention: Enabling this feature dramatically improves the speed of reasoning
- Reduction of num_generations: Reduces memory consumption and is suitable for resource-limited situations
- Using ONNX: Consider converting models to ONNX format to improve performance
Solutions to insufficient resources
For GPUs with smaller video memory, try:
- Downsizing the model
- Use smaller input resolution
- Reduce the number of queries processed simultaneously
This answer comes from the articleVLM-R1: A Visual Language Model for Localizing Image Targets through Natural LanguageThe
May not be reproduced without permission:AI productivity tools " What kind of hardware configuration is required to run VLM-R1? What are the performance optimization recommendations?
