Microsoft Announces Maia 300, Its Next AI Inference Chip

Microsoft announced Maia 300 on October 12, 2026 — the next generation of its custom Azure Maia AI inference platform — and said it will scale Azure Maia to multi-gigawatt deployments starting in 2027, making Maia the primary inference system for Microsoft products and services.

What Microsoft announced

In an official Microsoft Blog post by Scott Guthrie, executive vice president for Cloud + AI, the company said Maia 200 is already serving production workloads, including Microsoft Copilot, with GitHub Copilot and Microsoft Foundry to follow by the end of the year. Maia 200 runs OpenAI models, Microsoft’s own MAI models, and leading open-source model families.

Maia 300 is described as a complete inference-first system aimed at new workload shapes: long-context reasoning, latency-sensitive conversational experiences, and sustained multi-step agentic tasks.

The numbers Microsoft is putting forward

From the same announcement:

• Maia 300 is expected to deliver about 40% more tokens per megawatt compared with leading GPU-based systems.
• In a full 96-accelerator rack configuration, Maia 300 is expected to deliver up to 7× the rack-scale token throughput of Maia 200.
• Maia 200 already delivers up to 1.7× more tokens per watt for built-in AI capabilities in Teams, Copilot, and related experiences compared with leading GPU infrastructure, according to Microsoft.

Those efficiency claims are Microsoft’s forward-looking expectations for Maia 300, not independent third-party benchmarks published with the launch.

Architecture notes

A companion Azure Blog deep dive says Maia 300 uses a multi-chiplet design with compute on TSMC’s N2P process, eight HBM4 stacks providing 288 GB of capacity and 18 TB/s of peak memory bandwidth per accelerator within a 950-watt TDP, and racks that pack 96 accelerators inside a 167-kilowatt power envelope. The scale-up domain is described as growing to a superpod of up to 12,288 accelerators.

Timeline

Microsoft says Maia 300 will begin production deployments in 2027. Until then, Maia 200 remains the generation already in the wild.

Why it matters

Custom silicon is how hyperscalers try to serve more AI for the same power bill. If Maia becomes Microsoft’s primary inference path, that shifts how much Azure depends on third-party GPUs for Copilot-class traffic — without meaning Nvidia disappears from Azure overnight. The open question is how Maia 300’s claimed tokens-per-watt plays out once it is in production racks, not slide decks.