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Nvidia outperformed all expectations, reporting soaring profits Wednesday thanks to its graphics processing units that excel at AI workloads. But more categories of AI chips are gaining ground. Custom ASICs, or application-specific integrated circuits, are now being designed by all the major hyperscalers, from Google's TPU to Amazon's Trainium and OpenAI's plans with Broadcom . These chips are smaller, cheaper, accessible and could reduce these companies' reliance on Nvidia GPUs. Daniel Newman of the Futurum Group told CNBC that he sees custom ASICs "growing even faster than the GPU market over the next few years." Besides GPUs and ASICs, there are also field-programmable gate arrays, which can be reconfigured with software after they're made for use in all sorts of applications, like signal processing, networking and AI. There's also an entire group of AI chips that power AI on devices rather than in the cloud. Qualcomm , Apple and others have championed those on-device AI chips. CNBC talked to experts and insiders at the Big Tech companies to break down the crowded space and the various kinds of AI chips out there.
GPUs for general compute
Once used primarily for gaming, GPUs made Nvidia the world's most valuable public company after their use shifted toward AI workloads. Nvidia shipped some 6 million current-generation Blackwell GPUs over the past year.
Nvidia senior director of AI infrastructure Dion Harris shows CNBC's Katie Tarasov how 72 Blackwell GPUs work together as one in a GB200 NVL72 rack-scale server system for AI at Nvidia headquarters in Santa Clara, California, on November 12, 2025. Marc Ganley
The shift from gaming to AI started around 2012, when Nvidia's GPUs were used by researchers to build AlexNet, what many consider to be modern AI's big bang moment. AlexNet was a tool that was entered into a prominent image recognition contest. Whereas others in the contest used central processing units for their applications, AlexNet reliance on GPUs provided incredible accuracy and obliterated its competition. AlexNet's creators discovered that the same parallel processing that helps GPUs render lifelike graphics was also great for training neural networks, in which a computer learns from data rather than relying on a programmer's code. AlexNet showcased the potential of GPUs. Today, GPUs are often paired with CPUs and sold in server rack systems to be placed in data centers, where they run AI workloads in the cloud. CPUs have a small number of powerful cores running sequential general-purpose tasks, while GPUs have thousands of smaller cores more narrowly focused on parallel math like matrix multiplication. Because GPUs can perform many operations simultaneously, they're ideal for the two main phases of AI computation: training and inference. Training teaches the AI model to learn from patterns in large amounts of data, while inference uses the AI to make decisions based on new information. GPUs are the general-purpose workhorses of Nvidia and its top competitor, Advanced Micro Devices . Software is a major differentiator between the two GPU leaders. While Nvidia GPUs are tightly optimized around CUDA, Nvidia's proprietary software platform, AMD GPUs use a largely open-source software ecosystem. AMD and Nvidia sell their GPUs to cloud providers like Amazon, Microsoft , Google, Oracle and CoreWeave . Those companies then rent the GPUs to AI companies by the hour or minute. Anthropic's $30 billion deal with Nvidia and Microsoft, for example, includes 1 gigawatt of compute capacity on Nvidia GPUs. AMD has also recently landed big commitments from OpenAI and Oracle. Nvidia also sells directly to AI companies, like a recent deal to sell at least 4 million GPUs to OpenAI, and to foreign governments, including South Korea, Saudi Arabia and the U.K. The chipmaker told CNBC that it charges around $3 million for one of its server racks with 72 Blackwell GPUs acting as one, and ships about 1,000 each week. Dion Harris, Nvidia's senior director of AI infrastructure, told CNBC he couldn't have imagined this much demand when he joined Nvidia over eight years ago. "When we were talking to people about building a system that had eight GPUs, they thought that was overkill," he said.
ASICs for custom cloud AI
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