This article dives into NVIDIA's datacenter GPUs, organizing them by architecture—Pascal, Volta, and Ampere—and by interface type, such as PCIe and SXM. It details key features like CUDA cores, memory bandwidth, and power consumption for each model. The article highlights the crucial differences between PCIe and SXM interfaces, emphasizing SXM's advantage in enabling faster inter-GPU communication, which is essential for training large-scale AI models. It also provides practical guidance on selecting the right GPU based on specific computational needs, considering factors like memory capacity and precision requirements.

The article further explores NVIDIA’s high-performance GPU lineup, including the A100 (Ampere architecture) and the H100/H200 series (Hopper architecture). It provides an in-depth look at their specifications—such as memory size, bandwidth, CUDA cores, and power consumption—and highlights interface options like PCIe, SXM4, SXM5, and NVL. Additionally, the article introduces NVIDIA Superchips, which pair Grace CPUs with one or two datacenter GPUs to boost performance and minimize bottlenecks in demanding tasks like AI and HPC. These Superchips are especially powerful for large language model (LLM) inference, leveraging NVLink for ultra-fast communication between the CPU and GPU.

You can ⁠listen to the podcast part 1 and part 2 generated by NotebookLM based on the article⁠. In addition, I shared my experience of building an AI Deep learning workstation in ⁠another article⁠. If the experience of a DIY workstation peeks your interest, I am working on a site to compare GPUs.