CONFIRMED: Microsoft announced Maia 300, its next in-house AI chip system, on its official blog on 12 October 2026. All performance comparisons below are Microsoft’s own claims or expectations and have not been independently tested (COMPANY CLAIM).
Microsoft says it will make its own Azure Maia chips the main system it uses to run AI for its products, at very large scale. In a blog post on 12 October 2026, Scott Guthrie, Executive Vice President of Cloud + AI, wrote [1]:
“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 did not give a total gigawatt figure, a number of sites or a cost.
What is inference? Training is how an AI model is built. Inference is the work of running a finished model to answer requests, such as a Copilot reply. Every answer costs computing power and electricity.
Maia 200 is already in production
Microsoft says Maia 200 “is now serving Microsoft production workloads, including Microsoft Copilot”, with GitHub Copilot and Microsoft Foundry “to follow by end of year” [1]. It says Maia 200 runs OpenAI models, Microsoft’s own MAI models and open-source models, and is deploying across the United States, Europe and Australia [1].
COMPANY CLAIM: Microsoft says Maia 200 delivers “up to 1.7x more tokens per watt” for built-in AI features in Microsoft Teams, Copilot and Cowork, “compared with leading GPU infrastructure” [1]. It does not say which GPUs.
What are tokens per watt? AI models read and write text in small pieces called tokens. Tokens per watt measures how much AI output a system produces for each unit of power. Higher means more work for the same electricity.
Maia 300: what Microsoft announced
Microsoft calls Maia 300 the third generation of its Maia program, with an “inference-first architecture” built for long conversations, fast replies and multi-step “agentic” tasks [1].
COMPANY CLAIM: “we expect Maia 300 to deliver 40% more tokens per megawatt compared to leading GPU-based systems,” Microsoft says. In a full 96-accelerator setup, it says Maia 300 “delivers up to 7x the rack-scale token throughput of Maia 200” [1].
Specs Microsoft gave [1]:
- Eight HBM4 memory stacks with 18.0 TB/s of peak memory bandwidth, within a 950-watt TDP (the chip’s rated power and heat budget).
- A multi-chiplet design, with compute chiplets built on TSMC’s N2P process. Chiplets are smaller pieces of silicon combined into one chip.
- Support for low-precision number formats, including “enhanced MXFP4”, which Microsoft says cuts memory and bandwidth needs.
- Ethernet-based networking linking chips across racks.
- Racks of 96 accelerators within a 167-kilowatt power envelope, with 27.6 TB of HBM and 1.7 PB/s of total HBM bandwidth.
What is HBM? High-bandwidth memory is fast memory stacked in layers right next to the chip. AI inference often waits on memory, so how quickly data moves in and out matters as much as raw compute. Microsoft says “moving data efficiently, rather than raw compute power, is increasingly the bottleneck in large inference systems” [1].
Microsoft also describes Maia 300 as delivering “industry-leading HBM bandwidth per watt” and “a significant performance-per-watt advantage over traditional GPU architectures” (COMPANY CLAIM) [1].
When
“Maia 300 will begin production deployments in 2027,” Microsoft says [1]. It says it will share more at Microsoft Ignite 2026 in San Francisco next month [1].
Sources
- Scott Guthrie, “Microsoft to expand Azure Maia system to multi-gigawatt deployments”, The Official Microsoft Blog, 12 October 2026. https://blogs.microsoft.com/blog/2026/10/12/microsoft-to-expand-azure-maia-system-to-multi-gigawatt-deployments/
