A full evaluation with PhysUniBenchmark requires appropriate computing resource support. The tool recommends running model inference with a device equipped with an NVIDIA GPU to speed up processing. While CPU mode can be run, the processing speed is significantly reduced. Typical hardware configuration requirements include 16GB or more of RAM and at least 10GB of storage.
For the software environment, the tool is developed based on Python, requiring version 3.8 or higher and relying on scientific computing libraries such as NumPy and Pandas. For models that use cloud-based APIs (e.g., GPT-4o), a stable network connection and proper API configuration are also required.
The tool provides a comprehensive environment testing script to check whether the system meets the basic requirements before use, and guides the user through the necessary configuration steps to lower the threshold of entry.
This answer comes from the articlePhysUniBenchmark: benchmarking tool for multimodal physics problemsThe
