Microsoft says its Maia chips will become its main inference platform at multi-gigawatt scale from 2027

Microsoft will scale its Maia accelerator family to multi-gigawatt deployments starting in 2027, and Maia will become the primary inference system for Microsoft's own products and services, the Russian-language channel AI ML Big Data reported. Dataquest India confirmed the plan. Microsoft also unveiled Azure Maia 300, its third-generation custom accelerator, on October 12.

The disclosed specifications target serving, not training. According to WindowsForum's summary of the launch, Maia 300 uses a TSMC N2P chiplet design with eight HBM4 stacks delivering 18.0 TB/s of memory bandwidth and improved MXFP4 support. A rack holds 96 accelerators. Microsoft claims up to 7x the rack-scale token throughput of Maia 200. Interesting Engineering put the rack at 167 kW. Satya Nadella, Microsoft's chief executive, said the chip should deliver 40% more tokens per watt than leading GPU-based systems. Microsoft has not named the GPU baseline or the workload behind that figure.

Maia 200 is already in production in the United States, Europe and Australia and serves Microsoft Copilot. Microsoft told investors that running OpenAI and internal models on Maia 200 cuts operating costs by 30% to 40% compared with top Nvidia hardware, RuntimeWire reported. The same outlet said Microsoft has sought TSMC capacity for more than 300,000 Maia 300 chips for 2027 delivery and wants capacity for more than one million over time.

Bandwidth and low-precision number formats set decode throughput, and Microsoft optimized for both. Eighteen TB/s per package and native MXFP4 support favor memory-bound token generation for mixture-of-experts (MoE) models with large key-value (KV) caches. That is the workload that dominates Copilot and Azure OpenAI traffic.