The most powerful NVIDIA datacenter GPUs and Superchips

This article provides an in-depth overview of NVIDIA’s datacenter GPUs, categorizing them by architecture—Pascal, Volta, and Ampere—and interface types such as PCIe and SXM. It highlights essential features, including CUDA cores, memory bandwidth, and power consumption, for each model. Special attention is given to the differences between PCIe and SXM interfaces, with SXM standing out for its ability to enable faster inter-GPU communication—an advantage critical for training large-scale AI models. The article also guides readers in choosing the right GPU by considering specific requirements such as memory capacity and precision support.

The discussion moves on to NVIDIA’s flagship GPUs, including the A100 (Ampere architecture) and the latest H100/H200 series (Hopper architecture). Detailed specifications such as memory size, bandwidth, CUDA cores, and power consumption are outlined, along with interface options like PCIe, SXM4, SXM5, and NVL. The article also introduces NVIDIA Superchips, which integrate Grace CPUs with one or two datacenter GPUs to deliver groundbreaking performance. These Superchips are tailored for demanding workloads like AI, HPC, and large language model (LLM) inference, leveraging NVLink technology to enable high-speed communication between the CPU and GPU, effectively eliminating bottlenecks.

The article serves as a comprehensive guide for understanding NVIDIA’s cutting-edge GPU solutions, helping readers identify the most suitable technologies for their advanced AI and HPC applications.

Listen to the podcast part 1 and part 2 generated based on the article by NotebookLM.

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 and buy GPUs.

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