According to the project's technical documentation, specific hardware conditions need to be met when the model is running: benchmarks show that 6.4GB of video memory is required for model loading in FP16 accuracy, and 7.2GB is required for BF16 mode. actual deployment suggests the use of an NVIDIA 30/40 series graphics card to ensure support for the Tensor Core and the BF16 compute instructions. If you use a consumer graphics card such as RTX3060 (12GB), you can control the memory consumption by adjusting the num_generations parameter.
The project provides detailed performance optimization suggestions: enabling Flash Attention can increase the speed of attention computation by 3.2 times; using Deepspeed Zero-3 stage optimizer can reduce the graphics memory consumption by 40%. For resource-constrained scenarios, the document recommends the LoRA fine-tuning scheme, which requires only 2GB of video memory to complete the model adaptation, and the accuracy loss is controlled within 5%.
This answer comes from the articleVLM-R1: A Visual Language Model for Localizing Image Targets through Natural LanguageThe
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