微软发布第三代自研 AI 芯片 Azure Maia 300，Maia 200 已量产支撑 Copilot
Microsoft Pushes Custom AI Chips with Azure Maia 300
微软发布第三代自研 AI 加速芯片 Maia 300，并宣布 Maia 200 已投产，用于支撑 Copilot 等云服务。Maia 200 部署在美国、欧洲和澳大利亚的数据中心，每瓦 token 吞吐量最高为领先 GPU 基础设施的 1.7 倍，年底前还将支撑 GitHub Copilot 和 Microsoft Foundry。Maia 300 计划 2027 年投产，采用推理优先架构，每兆瓦 token 吞吐量预计比领先 GPU 系统高 40%，96 加速器整机架的 token 吞吐量最高可达 Maia 200 的 7 倍；微软计划 2027 年以多吉瓦规模扩展 Azure Maia 系列，目前 Maia 尚未通过 Azure 实例向客户开放。
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Microsoft unveiled Maia 300, its third-generation AI chip, as Maia 200 goes live, powering Copilot.
October 13, 2026
Microsoft on Monday (Oct. 12) unveiled Maia 300, its third-generation in-house AI accelerator, and said that Maia 200 is now in production, powering Copilot and other Microsoft cloud services.
The company said it plans to scale deployments of its Azure Maia processor family at multi-gigawatt scale in 2027, positioning Maia as a primary inference system for Microsoft products and services, according to a blog post by Scott Guthrie, Microsoft’s executive vice president, cloud + AI.
Microsoft said Maia 200 is deployed across its data centers in the US, Europe, and Australia, delivers up to 1.7 times more tokens per watt than leading GPU infrastructure, and powers built-in AI capabilities in Microsoft Teams, Copilot, and Cowork. By the end of the year, the chip is also expected to power GitHub Copilot and Microsoft Foundry, a platform for developing, deploying, and managing AI applications and agents, according to Guthrie’s blog post.
Maia 300, which Microsoft said will be deployed in production in 2027, is designed with an inference-first architecture optimized “for long-context, memory-intensive, and decode-heavy AI workloads,” according to the blog post. The company said the chip is expected to deliver 40% more tokens per megawatt than leading GPU-based systems and, in a full 96-accelerator rack, up to seven times the rack-scale token throughput of Maia 200.
Like other hyperscalers, Microsoft continues to rely heavily on Nvidia and AMD GPUs for AI workloads. At the same time, it has spent the last few years building its own custom silicon to improve performance and efficiency across its infrastructure and cloud services.
Unlike Amazon Web Services, which offers Trainium and Inferentia chips via public cloud instances, and Google Cloud, which provides access to its TPUs, Microsoft has not made Maia available to customer workloads via Azure instances. In Monday’s blog post, the company did not say whether that could change in the future.
Because Microsoft is using Maia primarily for internal workloads, the competitive dynamic is different from a traditional chip war among hyperscalers, said Matt Kimball, vice president and principal analyst at Moor Insights & Strategy. This is about Microsoft and other cloud providers delivering services faster, more efficiently, and more reliably.
“That’s good for the customer because they’re getting better quality of service, and it’s good for the cloud provider because they’re doing it at lower cost, which means they’re more profitable,” Kimball said.
Related:AMD to Acquire World Labs for $8.2B to Advance AI Models and Robotics
Kimball said that Maia 300 appears to represent a significant step forward in Microsoft’s custom silicon strategy. “Maia 100 was a proof of concept. Then 200 came out, and they solved the issues they had seen in the 100. They are powering GitHub and Copilot,” he said. “It validated the design choices they were making, and 300 was where they said, ‘Let’s go big,’ and that’s what you are seeing with the Maia 300.”
Microsoft’s plan to deploy the Azure Maia family at multi-gigawatt scale suggests broad global adoption across its infrastructure, Kimball noted.
“It’s going to be used very broadly to support their workloads across the globe,” he said.
Wylie Wong is a journalist and freelance writer specializing in technology, business and sports. He previously worked at CNET, Computerworld and CRN and loves covering and learning about the advances and ever-changing dynamics of the technology industry. On the sports front, Wylie is co-author of Giants: Where Have You Gone, a where-are-they-now book on former San Francisco Giants. He previously launched and wrote a Giants blog for the San Jose Mercury News, and in recent years, has enjoyed writing about the intersection of technology and sports.
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