NVIDIA’s Data-Center Roadmap Reveals GB200 and GX200 GPUs for 2024-2025

October 10, 2023 by our News Team

NVIDIA has launched its official roadmap for annual data center GPU architectures, including the upcoming Hopper GH200, Blackwell GB200, and GX200, as well as a mysterious "X" architecture set for a 2025 launch.

  • NVIDIA plans to introduce new data center GPU architectures annually.
  • The upcoming Hopper GH200 GPU, set to launch in 2024, will be followed by the Blackwell GB200 processor between 2024 and 2025.
  • NVIDIA is focused on maintaining its dominance in the data center GPU space.

nVidia has shared its plans for annual updates to its data-center GPUs in its latest Investors Presentation. A slide from the presentation has caught the attention of tech analysts at SemiAnalysis, who have been closely following NVIDIA’s strategies in the data center market. The slide reveals details about NVIDIA’s incorporation of HBM3E memory, updates to PCI Express standards, and changes to its multi-GPU interconnect technologies.

While NVIDIA has been relatively quiet about sharing roadmaps recently, likely due to the challenges in aligning gaming and data-center GPU architectures, as well as increasing competition in the market, the company has launched its official roadmap spanning multiple years. However, NVIDIA remains cautious about specifying exact dates due to the complexities of coordinating plans with foundries and software deployment readiness.

According to the roadmap, NVIDIA plans to introduce new data center GPU architectures annually. The upcoming Hopper GH200 GPU, set to launch in 2024, will be followed by the Blackwell GB200 processor between 2024 and 2025. A year later, the GX200 GPU will be introduced. These are just codenames, with the actual products expected to have names like H200, B100, and X100.

The “B” designation represents Blackwell, an architecture designed for both data-center and gaming products. This roadmap confirms the existence of the GH200 for data center purposes, debunking earlier rumors of GB100/GB102. The GB200 is now officially confirmed as the expected launch.

There is also mention of a mysterious architecture referred to as “X,” which could either be named after a scientist or simply serve as a placeholder. While it’s too early to speculate, what’s important is that the X architecture is scheduled for a 2025 launch, accompanied by a new data center product called “X100.”

This roadmap suggests that the Blackwell data-center product is expected to launch between late 2024 and early 2025, potentially allowing more time for the introduction of the X architecture. It remains uncertain if the “X” designation will also be used for gaming products. However, it has been confirmed that Blackwell will debut as part of the Geforce RTX 50 series.

Overall, NVIDIA’s roadmap showcases its commitment to regular updates in the data center GPU space. While specific details and timelines are still subject to change, it’s clear that NVIDIA is focused on maintaining its dominance in this market.

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Background Information

About nVidia: NVIDIA has firmly established itself as a leader in the realm of client computing, continuously pushing the boundaries of innovation in graphics and AI technologies. With a deep commitment to enhancing user experiences, NVIDIA's client computing business focuses on delivering solutions that power everything from gaming and creative workloads to enterprise applications. for its GeForce graphics cards, the company has redefined high-performance gaming, setting industry standards for realistic visuals, fluid frame rates, and immersive experiences. Complementing its gaming expertise, NVIDIA's Quadro and NVIDIA RTX graphics cards cater to professionals in design, content creation, and scientific fields, enabling real-time ray tracing and AI-driven workflows that elevate productivity and creativity to unprecedented heights. By seamlessly integrating graphics, AI, and software, NVIDIA continues to shape the landscape of client computing, fostering innovation and immersive interactions in a rapidly evolving digital world.

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Technology Explained

Geforce: Geforce is a line of graphics processing units (GPUs) developed by Nvidia. It is the most popular GPU used in the computer industry today. Geforce GPUs are used in gaming PCs, workstations, and high-end laptops. They are also used in virtual reality systems, artificial intelligence, and deep learning applications. Geforce GPUs are designed to deliver high performance and power efficiency, making them ideal for gaming and other demanding applications. They are also capable of rendering high-resolution graphics and providing smooth, realistic visuals. Geforce GPUs are used in a variety of applications, from gaming to professional workstations, and are the preferred choice for many computer users.

GPU: GPU stands for Graphics Processing Unit and is a specialized type of processor designed to handle graphics-intensive tasks. It is used in the computer industry to render images, videos, and 3D graphics. GPUs are used in gaming consoles, PCs, and mobile devices to provide a smooth and immersive gaming experience. They are also used in the medical field to create 3D models of organs and tissues, and in the automotive industry to create virtual prototypes of cars. GPUs are also used in the field of artificial intelligence to process large amounts of data and create complex models. GPUs are becoming increasingly important in the computer industry as they are able to process large amounts of data quickly and efficiently.

HBM3E: HBM3E is the latest generation of high-bandwidth memory (HBM), a type of DRAM that is designed for artificial intelligence (AI) applications. HBM3E offers faster data transfer rates, higher density, and lower power consumption than previous HBM versions. HBM3E is developed by SK Hynix, a South Korean chipmaker, and is expected to enter mass production in 2024. HBM3E can achieve a speed of 1.15 TB/s and a capacity of 64 GB per stack. HBM3E is suitable for AI systems that require large amounts of data processing, such as deep learning, machine learning, and computer vision.

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