Microsoft Unveils Maia 300 AI Chip, Plans Multi-Gigawatt Rollout Starting 2027
Microsoft says its third-generation in-house AI chip will become the main engine behind Copilot and its other AI services, and claims it beats leading GPU systems on efficiency.
By 1EyeAI Desk ·
In an Official Microsoft Blog post on Monday (Oct. 12, 2026), Scott Guthrie, Microsoft’s executive vice president of Cloud + AI, announced Maia 300, the third generation of the company’s Azure Maia AI inference program. Microsoft says it will scale Azure Maia to multi-gigawatt deployments beginning in 2027, making Maia the primary inference system for Microsoft products and services. Maia 300 production deployments begin in 2027.
Microsoft also says its previous chip, Maia 200, is now serving production workloads, including Microsoft Copilot, and is deploying across the United States, Europe and Australia. GitHub Copilot and Microsoft Foundry will follow by the end of the year, the company says, and Maia 200 runs OpenAI models, Microsoft’s own MAI models and leading open-source models.
Microsoft’s published specs for Maia 300: eight HBM4 memory stacks with 18.0 TB/s of peak memory bandwidth in a 950-watt chip, a multi-chiplet design with compute chiplets on TSMC’s N2P process, and support for low-precision formats including enhanced MXFP4. A full rack holds 96 accelerators within a 167-kilowatt power envelope, with 27.6 TB of HBM.
Company claims (not independently verified): Microsoft claims Maia 300 will deliver 40% more tokens per megawatt than leading GPU-based systems, and up to 7x the rack-scale token throughput of Maia 200. It also claims Maia 200 delivers up to 1.7x more tokens per watt than leading GPU infrastructure for some built-in AI features in Teams and Copilot. No independent benchmarks are available yet. Microsoft says it will share more at Microsoft Ignite 2026 next month in San Francisco.
What this means for you: If you use Copilot, Teams or GitHub Copilot, more of your requests will run on Microsoft’s own chips. Microsoft’s pitch is that this makes AI cheaper and faster to serve, which could mean higher usage limits or lower prices over time—but that depends on its efficiency claims holding up in the real world.