- H100: Part of the NVIDIA Hopper architecture, this GPU offers accelerated computing, enhanced by its advanced memory capabilities, making it ideal for a variety of data center workloads. It includes the Transformer Engine, designed to speed up AI models, and MIG technology for partitioning into multiple instances (VentureBeat).
- H200: Also within the Hopper family, the H200 stands out for powering generative AI and high-performance computing with its first integration of HBM3e memory, designed to handle extensive and fast memory requirements crucial for advanced AI and scientific computing tasks (NVIDIA Newsroom).
- DGX: These AI-dedicated server systems utilize multiple H100 GPUs. Known for delivering robust AI performance, they are commonly used for diverse high-performance computing applications and AI model training.
- EGX: Focused on edge computing, the EGX platform brings AI processing capabilities directly to the network’s edge, part of NVIDIA's Certified Systems.
- GH200: A high-performance computing system that integrates the GH200 chipset, often utilized in supercomputing and AI training due to its capability to handle complex simulations and large datasets (NVIDIA Newsroom).
- L4, L40, L40S: These GPUs enhance AI, graphics, and media acceleration, with significant improvements over predecessors like the A40, catering to various data center demands.
- A2, A10, A16, A30, A40: Targeted for different computing needs, each model in this series serves distinct performance levels within NVIDIA’s data center GPU range, with the A40 noted for its high-end AI and graphics performance.
- T4: Earlier in NVIDIA’s data center GPU lineup, the T4 is engineered for AI and machine learning workloads, as well as general-purpose server GPU tasks.
- Blackwell: Representing the next leap in NVIDIA's GPU designs, this architecture focuses on integrating AI enhancements and improving energy efficiency, poised to power the next generation of products.
- Grace Blackwell: Utilizing the Blackwell architecture, the Grace Blackwell systems are state-of-the-art AI servers designed to address the most demanding AI and supercomputing challenges. These systems feature an innovative CPU-GPU setup that optimizes processing power and energy efficiency.
- Rubin - the next chipset beyond Blakwell
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7moThis is a great list for referencing, think I'll bookmark this list Tony. Cheers
innovator & engineering leader in carbon-friendly computing; building big, sustainable clouds
7mosaying this only somewhat tongue-in-cheek as simple naming will always struggle to succinctly explain technology differences that just aren’t succinct to explain … but I now regularly find myself in discussion with “civilian” or “crossover” audiences where I lay out how the naming & product families work - just like you do here - and I can feel the eye-rolling ! to which I often can only say “I didn’t invent this stuff, I just work here, I’m just the messenger” - it absolutely creates barriers to adoption when succinct naming & explaining just doesn’t do the job the tech on your 101 covers easily two orders of magnitude in performance (three ?) and over 10 years of engineering (15 ?) - just not gonna be succinct
innovator & engineering leader in carbon-friendly computing; building big, sustainable clouds
7mothanks Tony for sharing this - now do the networking product line 😳😎