2 hours ago
Azure Maia 200 now in production; Azure Maia 300 coming in 2027
As we move into the era of Super Intelligence, Microsoft is entering a new phase of infrastructure. 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.
The multigenerational Azure Maia program has become a foundational element of Microsoft’s AI infrastructure strategy. Maia 200 is now serving Microsoft production workloads, including Microsoft Copilot, and supporting the MAI team as they develop next-generation models, with GitHub Copilot and Microsoft Foundry to follow by end of year. Across these workloads, Maia 200 runs OpenAI models, Microsoft’s own MAI models and leading open-source model families. Insights from the scale of Microsoft’s AI services allow for unique co-design between hardware systems and software applications to optimize real-world use cases across enterprise applications and industry scenarios.
Maia 300 is 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. By improving efficiency across the system, we expect Maia 300 to deliver 40% more tokens per megawatt compared to leading GPU-based systems. And in a full 96-accelerator configuration, it delivers up to 7x the rack-scale token throughput of Maia 200.
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Maia 200: Driving the workloads that Microsoft runs at scale
The Azure Maia system is designed around the real-world workloads Microsoft runs at scale and the models we expect to build in the years ahead. Because silicon development takes years, our software and hardware teams collaborate early to anticipate architectures, scale and capabilities, translating those requirements into greater memory capacity, higher bandwidth and more accelerators operating as a single system.
That co-engineering is already delivering measurable efficiency gains. Maia 200 is deploying across the United States, Europe and Australia and delivering up to 1.7x more tokens per watt for built-in AI capabilities in Microsoft Teams, Copilot and Cowork experiences compared with leading GPU infrastructure.
Experience running our AI models and services on Maia 200 directly informed the design of Maia 300. Together, the two generations are designed to support a broad set of additional Microsoft’s AI services: multi-step agentic work that will help GitHub deliver coding experiences more efficiently; cloud-scale model serving that will help Microsoft Foundry support diverse architectures and deployment environments; inference and data generation for the MAI team; and latency-sensitive interactions that will help Dragon Copilot scale workflows and reduce clinicians’ administrative burden.
Maia 300: Scaling more useful AI within every megawatt
AI is becoming a foundational layer of every application, workflow and business process. As enterprises move beyond chat experiences to systems of agents that can reason, act and collaborate, AI infrastructure must deliver intelligence securely and efficiently at scale while continuously adapting as models and workloads evolve.
As the third generation of our Azure Maia program, Maia 300 is designed with an inference-first architecture optimized for long-context, memory-intensive and decode-heavy AI workloads. This involves balancing compute, memory bandwidth, networking and power efficiency at cloud scale.
![[Image: OMB-Maia-200-Image-1.png.jpg]](https://blogs.microsoft.com/wp-content/uploads/2026/10/OMB-Maia-200-Image-1.png.jpg)
Azure Maia 300 system
Key capabilities of the Maia 300 system include:
Less data movement, more useful compute: Moving data efficiently, rather than raw compute power, is increasingly the bottleneck in large inference systems. Maia 300’s software-defined dataflow architecture and AI-assisted kernel optimization work together to maximize locality, reduce data movement and improve utilization across compute, memory and networking resources. By treating dataflow as a first-class design principle, Maia 300 delivers a significant performance-per-watt advantage over traditional GPU architectures.
Higher memory bandwidth within every watt: As AI workloads become increasingly context and memory-bound, Maia 300 delivers industry-leading HBM bandwidth per watt. Eight HBM4 stacks provide 18.0 TB/s of peak memory bandwidth within a 950-watt TDP, enabling efficient execution of large models and long-context workloads. Specialized kernel support uses on-die SRAM as a software-managed scratchpad, reducing pressure on HBM.
Chiplet architecture for enhanced efficiency: A new multi-chiplet architecture separates compute, memory-controller and I/O functions, improving efficiency, scalability and cost structure. Compute chiplets are built on TSMC’s N2P process node to maximize performance-per-watt.
Larger context, richer intelligence: By designing for advanced low-precision formats, including enhanced MXFP4, Maia 300 reduces memory and bandwidth requirements for both computation and KV-cache storage. The result is larger context windows, higher throughput and improved model efficiency within the same power envelope.
Scale-up networking at datacenter scale: Maia 300 treats networking as a seamless extension of the software-defined dataflow architecture. Rather than moving data through separate communication layers, dataflow operations span compute, memory and network resources as a single system. High-bandwidth interconnects between Maia accelerators are optimized for tensor parallelism, while a custom AI transport delivers performant and reliable scale-up communication across accelerators and racks. The AI-optimized transport leverages Ethernet-based networking infrastructure to enable a unified topology that scales efficiently from a single rack to datacenter-scale deployments.
Higher AI throughput from every rack: Maia 300 rack systems bring together 96 accelerators within a 167-kilowatt power envelope, delivering 27.6 TB of HBM capacity and 1.7 PB/s of aggregate HBM bandwidth across a high-speed scale-up domain. That density allows Microsoft to serve more simultaneous AI interactions and larger workloads from each rack, improving datacenter capacity and the economics of expanding AI services.
Maia 300 will begin production deployments in 2027. For more information on Maia 300 architectural details, read our technical paper.
![[Image: Maia-300-Specifications.jpg]](https://blogs.microsoft.com/wp-content/uploads/2026/10/Maia-300-Specifications.jpg)
Maia 300 at a glance
Azure Maia is at the heart of our Frontier Transformation
The future will not be defined by a single model or a single piece of silicon or a single innovation. Model and silicon choice are inherent in our vision for Frontier Transformation. Our focus is on developing responsible, affordable and scalable platforms that will help everyone amplify their intelligence to drive breakthrough outcomes that matter for them.
Microsoft is unique because we are able to optimize from silicon to systems to agent on an open, heterogenous and model diverse platform our customers can count on for their AI transformation, today and in the years ahead. This platform is at the heart of Frontier Transformation, and Azure Maia is a foundational component of our approach to expand access, accelerate innovation and help customers capture more value from AI.
Next month at Microsoft Ignite 2026 in San Francisco and online we’re looking forward to sharing more information on our strategy, technology adoption trends and success in Frontier Transformation.
For more details, photos and videos, visit our Maia 300 site.
Scott Guthrie is responsible for a set of hyperscale cloud computing solutions and services including Azure, Microsoft’s cloud computing platform, generative AI solutions, data platforms and information and cybersecurity. These platforms and services help organizations across the globe solve urgent challenges — and transform for the future.
The post Microsoft to expand Azure Maia system to multi-gigawatt deployments appeared first on The Official Microsoft Blog.
As we move into the era of Super Intelligence, Microsoft is entering a new phase of infrastructure. 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.
The multigenerational Azure Maia program has become a foundational element of Microsoft’s AI infrastructure strategy. Maia 200 is now serving Microsoft production workloads, including Microsoft Copilot, and supporting the MAI team as they develop next-generation models, with GitHub Copilot and Microsoft Foundry to follow by end of year. Across these workloads, Maia 200 runs OpenAI models, Microsoft’s own MAI models and leading open-source model families. Insights from the scale of Microsoft’s AI services allow for unique co-design between hardware systems and software applications to optimize real-world use cases across enterprise applications and industry scenarios.
Maia 300 is 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. By improving efficiency across the system, we expect Maia 300 to deliver 40% more tokens per megawatt compared to leading GPU-based systems. And in a full 96-accelerator configuration, it delivers up to 7x the rack-scale token throughput of Maia 200.
YouTube Video
Click here to load media
Maia 200: Driving the workloads that Microsoft runs at scale
The Azure Maia system is designed around the real-world workloads Microsoft runs at scale and the models we expect to build in the years ahead. Because silicon development takes years, our software and hardware teams collaborate early to anticipate architectures, scale and capabilities, translating those requirements into greater memory capacity, higher bandwidth and more accelerators operating as a single system.
That co-engineering is already delivering measurable efficiency gains. Maia 200 is deploying across the United States, Europe and Australia and delivering up to 1.7x more tokens per watt for built-in AI capabilities in Microsoft Teams, Copilot and Cowork experiences compared with leading GPU infrastructure.
Experience running our AI models and services on Maia 200 directly informed the design of Maia 300. Together, the two generations are designed to support a broad set of additional Microsoft’s AI services: multi-step agentic work that will help GitHub deliver coding experiences more efficiently; cloud-scale model serving that will help Microsoft Foundry support diverse architectures and deployment environments; inference and data generation for the MAI team; and latency-sensitive interactions that will help Dragon Copilot scale workflows and reduce clinicians’ administrative burden.
Maia 300: Scaling more useful AI within every megawatt
AI is becoming a foundational layer of every application, workflow and business process. As enterprises move beyond chat experiences to systems of agents that can reason, act and collaborate, AI infrastructure must deliver intelligence securely and efficiently at scale while continuously adapting as models and workloads evolve.
As the third generation of our Azure Maia program, Maia 300 is designed with an inference-first architecture optimized for long-context, memory-intensive and decode-heavy AI workloads. This involves balancing compute, memory bandwidth, networking and power efficiency at cloud scale.
Azure Maia 300 systemKey capabilities of the Maia 300 system include:
Less data movement, more useful compute: Moving data efficiently, rather than raw compute power, is increasingly the bottleneck in large inference systems. Maia 300’s software-defined dataflow architecture and AI-assisted kernel optimization work together to maximize locality, reduce data movement and improve utilization across compute, memory and networking resources. By treating dataflow as a first-class design principle, Maia 300 delivers a significant performance-per-watt advantage over traditional GPU architectures.
Higher memory bandwidth within every watt: As AI workloads become increasingly context and memory-bound, Maia 300 delivers industry-leading HBM bandwidth per watt. Eight HBM4 stacks provide 18.0 TB/s of peak memory bandwidth within a 950-watt TDP, enabling efficient execution of large models and long-context workloads. Specialized kernel support uses on-die SRAM as a software-managed scratchpad, reducing pressure on HBM.
Chiplet architecture for enhanced efficiency: A new multi-chiplet architecture separates compute, memory-controller and I/O functions, improving efficiency, scalability and cost structure. Compute chiplets are built on TSMC’s N2P process node to maximize performance-per-watt.
Larger context, richer intelligence: By designing for advanced low-precision formats, including enhanced MXFP4, Maia 300 reduces memory and bandwidth requirements for both computation and KV-cache storage. The result is larger context windows, higher throughput and improved model efficiency within the same power envelope.
Scale-up networking at datacenter scale: Maia 300 treats networking as a seamless extension of the software-defined dataflow architecture. Rather than moving data through separate communication layers, dataflow operations span compute, memory and network resources as a single system. High-bandwidth interconnects between Maia accelerators are optimized for tensor parallelism, while a custom AI transport delivers performant and reliable scale-up communication across accelerators and racks. The AI-optimized transport leverages Ethernet-based networking infrastructure to enable a unified topology that scales efficiently from a single rack to datacenter-scale deployments.
Higher AI throughput from every rack: Maia 300 rack systems bring together 96 accelerators within a 167-kilowatt power envelope, delivering 27.6 TB of HBM capacity and 1.7 PB/s of aggregate HBM bandwidth across a high-speed scale-up domain. That density allows Microsoft to serve more simultaneous AI interactions and larger workloads from each rack, improving datacenter capacity and the economics of expanding AI services.
Maia 300 will begin production deployments in 2027. For more information on Maia 300 architectural details, read our technical paper.
Maia 300 at a glanceAzure Maia is at the heart of our Frontier Transformation
The future will not be defined by a single model or a single piece of silicon or a single innovation. Model and silicon choice are inherent in our vision for Frontier Transformation. Our focus is on developing responsible, affordable and scalable platforms that will help everyone amplify their intelligence to drive breakthrough outcomes that matter for them.
Microsoft is unique because we are able to optimize from silicon to systems to agent on an open, heterogenous and model diverse platform our customers can count on for their AI transformation, today and in the years ahead. This platform is at the heart of Frontier Transformation, and Azure Maia is a foundational component of our approach to expand access, accelerate innovation and help customers capture more value from AI.
Next month at Microsoft Ignite 2026 in San Francisco and online we’re looking forward to sharing more information on our strategy, technology adoption trends and success in Frontier Transformation.
For more details, photos and videos, visit our Maia 300 site.
Scott Guthrie is responsible for a set of hyperscale cloud computing solutions and services including Azure, Microsoft’s cloud computing platform, generative AI solutions, data platforms and information and cybersecurity. These platforms and services help organizations across the globe solve urgent challenges — and transform for the future.
The post Microsoft to expand Azure Maia system to multi-gigawatt deployments appeared first on The Official Microsoft Blog.