Google could build more AI accelerators than Nvidia sells in 2028, analyst claims — could push the company to use Intel Foundry to meet its goals

Google could build more AI accelerators than Nvidia sells in 2028, analyst claims — could push the company to use Intel Foundry to meet its goals

Meanwhile, for Google's rivals, the milestone will underscore the growing importance of vertically integrated AI infrastructure, where cloud providers design chips tailored to their own software stacks, workloads, and data centers instead of purchasing off-the-shelf GPUs.

Yet, Google's surpassing Nvidia in unit shipments would not necessarily diminish Nvidia's dominant position. AI demand continues to expand so rapidly that both companies could increase deployments simultaneously, but Google will simply grow faster, at least till Nvidia ups production of its AI accelerators with Feynman and Feynman Ultra in 2029 – 2030. After all, Nvidia's AI GPUs are sold out. What Nvidia should worry about is not the volumes of TPUs that Google can deploy, but rather the fact that these processors do rely on a software stack that rivals Nvidia's CUDA, the company's main competitive advantage.

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Anton Shilov is a contributing writer at Tom\u2019s Hardware. Over the past couple of decades, he has covered everything from CPUs and GPUs to supercomputers and from modern process technologies and latest fab tools to high-tech industry trends. ","collapsible":{"enabled":true,"maxHeight":250,"readMoreText":"Read more","readLessText":"Read less"}}), "https://slice.vanilla.futurecdn.net/13-4-25/js/authorBio.js"); } else { console.error('%c FTE ','background: #9306F9; color: #ffffff','no lazy slice hydration function available'); } Anton Shilov Social Links Navigation Contributing Writer Anton Shilov is a contributing writer at Tom’s Hardware. Over the past couple of decades, he has covered everything from CPUs and GPUs to supercomputers and from modern process technologies and latest fab tools to high-tech industry trends.

usertests Maybe they should make wafer-sized TPUs and use SRAM instead of HBM. Reply

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