Microsoft has put its custom Azure Maia 200 AI processor into production while detailing the architecture for its next Maia 300 system. The current chip handles live workloads across Microsoft Copilot and internal model training, with the company claiming 1.7x better token efficiency than competing GPU setups. The upgraded Maia 300 platform will enter production in 2027 to power multi gigawatt datacenter clusters.
Microsoft operates Maia 200 clusters across the United States, Europe, and Australia. The hardware powers Microsoft Copilot, internal OpenAI model serving, and open source architectures, with deployments planned for GitHub Copilot before the end of the year. Company benchmarks show the processor delivers 1.7x more tokens per watt for Teams and Copilot tasks compared to standard GPU hardware. Early performance data gathered from these live environments directly guided the design of the next platform generation.
The upcoming Maia 300 focuses on inference efficiency for long context reasoning and multi step agent tasks. Microsoft builds the compute chiplets on TSMC N2P process nodes while separating memory controllers and input output logic into distinct dies. Each Maia 300 accelerator features 8 HBM4 stacks that provide 18.0 TB per second of memory bandwidth within a 950 watt thermal design power budget. The design also adopts MXFP4 low precision data formats to expand context windows without increasing memory requirements.
At the datacenter level, Microsoft connects 96 accelerators into a single rack system running within a 167 kilowatt power envelope. A full rack offers 27.6 TB of HBM memory capacity and 1.7 PB per second of aggregate bandwidth over an Ethernet based interconnect transport. Microsoft projects this 96 chip setup will achieve 7x the token throughput of a Maia 200 rack and 40% more tokens per megawatt than competing GPU clusters when production shipments begin in 2027.
