This revision is from 2024/06/18 05:18. You can Restore it.

Example of a Multi-GPU Setup

For a high-end multi-GPU setup, consider the following:

1. Motherboard: A motherboard with multiple PCIe slots, preferably supporting PCIe 4.0 for higher bandwidth.
2. Power Supply Unit (PSU): A robust PSU with enough power and connectors for multiple GPUs.
3. Cooling Solutions: Adequate cooling (both air and liquid cooling options) to manage the heat output of multiple GPUs.

Configuration Tips

1. BIOS Settings: Ensure the BIOS is configured to support multi-GPU setups.
2. Driver Installation: Install the latest NVIDIA drivers that support multi-GPU configurations.
3. Framework Configuration: In your deep learning framework, configure the settings to utilize multiple GPUs (e.g., using torch.nn.DataParallel or torch.distributed in PyTorch).

Summary

By focusing on high VRAM, CUDA/Tensor cores, NVLink support, and efficient cooling, you can build a powerful multi-GPU setup capable of running large language models locally. Using high-end GPUs like the NVIDIA RTX 3090 or the A100 will provide the performance needed for demanding AI tasks.

Budget Build: Objective: Maximize performance while minimizing costs.