A Microsoft Azure Maia 300 prototype chip.
Microsoft
Microsoft will ramp up use of its homegrown artificial intelligence chips after years of reliance on Nvidia’s graphics processing units, as the software giant attempts to bolster its AI stack.
Starting with its third-generation Maia AI accelerator, unveiled on Monday, Microsoft said it will mainly rely on its own silicon to handle incoming requests.
Microsoft’s greatest strength in AI has been its Azure cloud, which runs a series of models for clients. Its Microsoft 365 Copilot and GitHub Copilot offer AI-powered services to software engineers and other office workers. And the company develops in-house models, though they’ve yet to make a big splash in a market dominated by OpenAI, Anthropic and Google.
The emerging opportunity in chips stems from unrelenting demand for advanced processors and a desire in the market for choice. Nvidia’s GPUs have been the standard for AI workloads and models, and they fill Microsoft’s existing data centers, while also serving to train models from OpenAI and Anthropic.
But as demand keeps growing, the model leaders have embraced a variety of silicon from companies including Amazon, Advanced Micro Devices, Cerebras, Google and, in recent months, Microsoft.
For general-purpose use, Microsoft already taps its own central processing units as well as silicon from Intel and AMD. Finance chief Amy Hood said at a Goldman Sachs conference last month that use of its own chips has helped with “margin improvement over the course of a decade.”
Microsoft introduced its inaugural AI chip, the Maia 100, in 2023. It touted the Maia 200 this past January, and CEO Satya Nadella told analysts in July that Maia 200 “delivers 30% better performance per dollar than the latest generation hardware in our fleet” and said it was handling OpenAI models.
Production of the Maia 300 starts in 2027. Microsoft is targeting multi-gigawatt deployments of chips in the Maia family, according to a blog post from Executive Vice President Scott Guthrie. But Alistair Speirs, a general manager, declined to provide a timeline.
“We reserve a little bit of optionality there, because this is an industry that’s changing very fast, and we want to deploy the right silicon for the right models that we’re using at the right time as well,” Speirs said.
Microsoft expects to pack 96 Maia 300 chips into each data center rack, double the number for the Maia 200. The new version can run through up to seven times more AI tokens per rack, with 40% more tokens per megawatt when compared with GPUs.
“We need new architectures that will give us essentially better performance, better use of the power that we have,” Speirs said. “You can think of power translating pretty directly into operational cost for something like a data center as well.”
Parts of Microsoft 365 Copilot should speed up with Maia 300, said Jim Kleewein, a technical fellow at the company.
In a note distributed to clients on Wednesday, Evercore ISI analysts estimated that Maia will represent about 10% of Microsoft’s spending on AI chips by the first quarter of 2027. The analysts recommend buying Microsoft shares.
Nvidia will still be a big part of the equation. CEO Jensen Huang spoke at an event last week where Microsoft announced a Surface laptop containing an Nvidia processor. And when it comes to training models, Microsoft partners with Nvidia and AMD, Speirs said.
Unlike Google’s tensor processing units or Amazon’s Inferentia and Trainium, Microsoft’s accelerators can’t be rented by the hour to other companies.
“Nobody has tested them outside of Microsoft yet,” said Patrick Moorhead, CEO of advisory firm Moor Insights and Strategy.
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