NVIDIA 101

I sometimes get confused about all the NVIDIA chipsets, so I made a cheat sheet I wanted to share.

1. H100: This is part of the NVIDIA Hopper architecture. The H100 Tensor Core GPU is designed for accelerated computing, offering significant performance and scalability improvements for a variety of data center workloads. It's known for its advanced memory capabilities and is the ninth generation of NVIDIA’s data center GPU.
2. H200: The H200 Tensor Core GPU, also based on the NVIDIA Hopper architecture, supercharges generative AI and HPC workloads. It stands out as the first GPU with HBM3e memory, which provides larger and faster memory to fuel generative AI and large language models (LLMs) and advances scientific computing for HPC workloads.
3. DGX: NVIDIA's DGX systems are AI-focused server systems. The DGX H100, for example, is a powerful AI server packing multiple H100 GPUs, known for delivering high AI performance. The DGX systems are used in various high-performance computing applications and AI model training.
4. EGX: Part of NVIDIA's Certified Systems, the EGX platform is designed for edge computing, bringing AI capabilities to the edge of the network.
5. GH200: The NVIDIA DGX GH200 is another high-performance computing system from NVIDIA, integrating the GH200 chipset. It's designed for complex simulations and large datasets, often used in supercomputing and AI training environments.
6. L4, L40, L40S: These are NVIDIA data center GPUs, part of their broader range of products for accelerated computing. The L40S, for instance, offers significant improvements in AI, graphics, and media acceleration over previous generations like the NVIDIA A40.
7. A2, A10, A16, A30, A40: These are also part of NVIDIA’s range of data center GPUs, each designed for specific computing needs and performance levels. For example, the A40 is known for its high-end performance in AI and graphics applications.
8. T4: The NVIDIA T4 GPU is part of their earlier range of data center GPUs, designed for AI and machine learning workloads, as well as general-purpose server GPU tasks.