AMD is targeting competitor Nvidia with its latest hardware release: a rack-scale system designed to meet the computing needs of the world’s largest AI labs. At the Advancing AI conference in San Francisco, AMD Chair and CEO Dr. Lisa Su presented the new AI rack system, named Helios, along with its expanding customer base, featuring Microsoft, as the company gears up for shipments later this year.
Helios combines multiple processors into a single high-powered unit optimized for data centers to train and operate AI models alongside other compute-intensive workloads. Su described Helios as the tech industry’s “highest-performance AI rack,” specifically built to handle the most demanding frontier models at scale. The system will be deployed by leading AI companies at gigawatt-scale.
Nvidia has historically dominated the AI rack market with its Vera Rubin and Grace Blackwell systems. However, early performance metrics indicate that Helios outperforms the Vera Rubin across several key areas, presenting AMD with a potential competitive edge, according to The Register.
Helios was initially revealed in 2025 and demonstrated at CES 2026. It has already garnered significant interest from prominent customers, including OpenAI, Meta, Oracle, Anthropic, and Microsoft. Microsoft CEO Satya Nadella announced plans to enhance the Azure infrastructure with Helios. Additionally, AMD and Anthropic formed a strategic partnership to deploy up to two gigawatts of GPUs using the Helios system.
In conjunction with the Helios launch, AMD unveiled its Venice-X CPU, designed for high-computing workloads in data centers, which is expected to debut in 2027. During her address, Su discussed the future of the chip industry, predicting that chips powering AI will significantly influence the overall computing market by 2030. This shift is driven by what she described as a “step change in compute demand” resulting from the rise of agentic AI.
Su elaborated on the growing complexity of AI tasks, stating, “When you ask the agent to do something, it actually has dozens of steps, and it has to reason, and it has to call tools, and it has to access data.” She emphasized the need for numerous GPUs to effectively tackle these processes.
Su forecasted that the AI accelerator market would reach approximately $1.4 trillion by 2030, potentially nearing the size of the current semiconductor market. “We do expect that GPUs are going to make up the vast majority of that market,” Su added. She noted that as algorithms evolve and workloads change, the demand for programmable solutions within the silicon ecosystem will continue to expand.
