Building a deep learning workstation

Cooling & Ventilation

Cooling and ventilation are crucial for maintaining optimal performance in a deep learning workstation. Daniel Whitenack explains the importance of choosing the right GPU style, such as blower or non-blower, to manage heat effectively. Positioning GPUs correctly can prevent overheating and performance degradation, as non-blower styles can blow heat onto adjacent cards 1. Daniel shares his experience of ordering multiple case fans to enhance airflow, emphasizing the significance of proper ventilation in GPU setups 1.

There's a bunch of options here as well. And there's two main types of cards. There's actually a blower style card which pulls in air from one side and actually shoots it through the card and out the back of the computer.

--- Daniel Whitenack

Additionally, Chris Benson2.

   

Multi-GPU Usage

Using multiple GPUs in a workstation requires careful consideration of setup logic and communication between the cards. Daniel discusses his strategy of running separate tasks on each GPU, allowing for efficient use of resources when collaborating with another developer 3. He notes the potential of using Nvidia's NVLink for faster data transfer between GPUs, which could enhance performance by reducing communication bottlenecks 3.

There's a special link between GPU cards called an NV link, which is, I think, in my understanding, the best way to communicate data between the two cards.

--- Daniel Whitenack

Chris and Daniel also touch on the challenges of acquiring GPUs and the importance of having a motherboard that supports expansion for future workloads 4.