Microsoft announces Maia 300, its third in-house AI chip, and plans multi-gigawatt Maia deployments from 2027
Confirmed
The takeaway
On Oct 12, 2026 Microsoft announced Maia 300, the third generation of its Azure Maia AI accelerator, built for inference (long-context reasoning and agentic tasks).
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- 3 of 5
- Last verified
- 12 October 2026
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Key facts
- Announced Oct 12, 2026 by Scott Guthrie, Microsoft EVP Cloud + AI; production deployments of Maia 300 begin in 2027
- Plan: ‘We will scale our Azure Maia family to multi-gigawatt deployments starting in 2027, making Maia the primary inference system for Microsoft products and services’
- Microsoft’s claims (not independently measured): 40% more tokens per megawatt than ‘leading GPU-based systems’; up to 7x the rack-scale token throughput of Maia 200 in the full 96-accelerator configuration; more than 2.5x Maia 200’s performance per watt
- Chip: multi-chiplet design; compute chiplets on TSMC’s N2P process; eight HBM4 stacks with 288 GB and 18 TB/s of memory bandwidth per accelerator within a 950-watt TDP; support for enhanced MXFP4 and sub-3-bit storage formats
- Rack: 96 accelerators in 24 four-accelerator trays within 167 kW (double Maia 200’s density), 27.6 TB of HBM; liquid cooling removes more than 80% of the heat
- Network: Ethernet-based custom transport; up to 128 racks form a ‘superpod’ of 12,288 accelerators
- Maia 200 (announced January 2026) now serves Microsoft Copilot and the MAI model team; GitHub Copilot and Microsoft Foundry follow by the end of 2026. It runs OpenAI models, Microsoft’s MAI models and open-source model families
- Microsoft says Maia 200 delivers up to 1.7x more tokens per watt than leading GPU infrastructure for AI features in Teams, Copilot and Cowork, and is deployed in the US, Europe and Australia
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- Mustafa Suleyman (Microsoft AI CEO), to The Verge: ‘At that scale we need a platform that we own, and that we co-design with our models.’
What happened
Scott Guthrie, who runs Microsoft’s Cloud + AI group, announced Maia 300 on the company blog, and the Azure hardware team published an architecture paper. Maia 300 is an inference system, not a training chip. Microsoft describes it as “a complete intelligent system designed for the new types of AI workloads emerging today: Long-context reasoning, latency-sensitive conversational experiences and sustained, multi-step agentic tasks.”
The design splits compute, memory-controller and I/O functions into separate chiplets, with compute on TSMC’s N2P node. Each accelerator has eight HBM4 stacks (288 GB, 18 TB/s). Compared with Maia 200, Microsoft states 36% more compute FLOPs, 2.5 times the memory bandwidth and 33% more memory capacity. A rack holds 96 accelerators in 167 kW, and an Ethernet-based network joins up to 128 racks into one scale-up domain of 12,288 accelerators.
Microsoft says the two Maia generations will serve GitHub coding agents, Microsoft Foundry model serving, inference and data generation for its MAI model team, and Dragon Copilot. The Reuters report was read only as a headline (the site blocks automated access); the numbers here come from Microsoft’s two posts and The Verge.
Why it matters
Microsoft is the largest buyer of Nvidia GPUs among cloud companies, and it now states that its own chip will be its main inference system from 2027. All performance figures are Microsoft’s own and compare against unnamed GPU systems. The announcement came nine months after Maia 200, which shows a faster release rhythm for hyperscaler chips, alongside Google’s TPUs, Amazon’s Trainium and the custom chips Broadcom builds for OpenAI and Anthropic.
Sources
4 sources from 4 sites. Numbers match the chips in the text.
4 sources: 2 primary, 2 press
Primary
- Official Microsoft Blog: Microsoft to expand Azure Maia system to multi-gigawatt deployments (Scott Guthrie)blogs.microsoft.com, official
- Azure Blog: Deep dive into Azure Maia 300 architectureazure.microsoft.com, official
Press
Changes
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