May 26, 2024


Because the tempo of AI and the transformation it permits throughout industries continues to speed up, Microsoft is dedicated to constructing and enhancing our international cloud infrastructure to fulfill the wants from clients and builders with quicker, extra performant, and extra environment friendly compute and AI options. Azure AI infrastructure contains expertise from industry leaders in addition to Microsoft’s personal improvements, together with Azure Maia 100, Microsoft’s first in-house AI accelerator, introduced in November. On this weblog, we are going to dive deeper into the expertise and journey of growing Azure Maia 100, the co-design of and software program from the bottom up, constructed to run cloud-based AI workloads and optimized for Azure AI infrastructure.

Azure Maia 100, pushing the boundaries of semiconductor innovation

Maia 100 was designed to run cloud-based AI workloads, and the design of the chip was knowledgeable by Microsoft’s expertise in working complicated and large-scale AI workloads equivalent to Microsoft Copilot. Maia 100 is among the largest processors made on 5nm node utilizing superior packaging expertise from TSMC.   

By means of collaboration with Azure clients and leaders within the semiconductor ecosystem, equivalent to foundry and EDA companions, we are going to proceed to use real-world workload necessities to our silicon design, optimizing your entire stack from silicon to service, and delivering the most effective expertise to our clients to empower them to attain extra.

Azure Maia 100, Microsoft’s first in-house AI accelerator

Finish-to-end methods optimization, designed for scalability and sustainability 

When growing the structure for the Azure Maia AI accelerator sequence, Microsoft reimagined the end-to-end stack in order that our methods may deal with frontier fashions extra effectively and in much less time. AI workloads demand infrastructure that’s dramatically totally different from different cloud compute workloads, requiring elevated energy, cooling, and networking functionality. Maia 100’s custom rack-level energy distribution and administration integrates with Azure infrastructure to attain dynamic energy optimization. Maia 100 servers are designed with a fully-custom, Ethernet-based community protocol with combination bandwidth of four.eight terabits per accelerator to allow higher scaling and end-to-end workload efficiency.  

Once we developed Maia 100, we additionally constructed a devoted “sidekick” to match the thermal profile of the chip and added rack-level, closed-loop liquid cooling to Maia 100 accelerators and their host CPUs to attain larger effectivity. This structure permits us to carry Maia 100 methods into our present datacenter infrastructure, and to suit extra servers into these services, all inside our present footprint. The Maia 100 sidekicks are additionally constructed and manufactured to fulfill our zero waste dedication. 

The server rack and cooling “sidekick” for Azure Maia 100

Co-optimizing and software program from the bottom up with the open-source ecosystem 

From the beginning, transparency and collaborative development have been core tenets in our design philosophy as we construct and develop Microsoft’s cloud infrastructure for compute and AI. Collaboration permits quicker iterative growth throughout the industry—and on the Maia 100 platform, we’ve cultivated an open neighborhood mindset from algorithmic information varieties to software program to .  

To make it simple to develop AI fashions on Azure AI infrastructure, Microsoft is creating the software program for Maia 100 that integrates with fashionable open-source frameworks like PyTorch and ONNX Runtime. The software program stack gives wealthy and complete libraries, compilers, and instruments to equip information scientists and builders to efficiently run their fashions on Maia 100. 

Diagram showing the software stack of Azure Maia 100

To optimize workload efficiency, AI sometimes requires growth of custom kernels which might be silicon-specific. We envision seamless interoperability amongst AI accelerators in Azure, so now we have built-in Triton from OpenAI. Triton is an open-source programming language that simplifies kernel authoring by abstracting the underlying . This may empower builders with full portability and adaptability with out sacrificing effectivity and the flexibility to focus on AI workloads. 

Diagram showing the seamless development experience on Azure AI infrastructure

Maia 100 can also be the primary implementation of the Microscaling (MX) information format, an industry-standardized information format that results in quicker mannequin coaching and inferencing instances. Microsoft has partnered with AMD, ARM, Intel, Meta, NVIDIA, and Qualcomm to launch the MX specification via the Open Compute Undertaking neighborhood in order that your entire AI ecosystem can profit from these algorithmic enhancements. 

Azure Maia 100 is a singular innovation combining state-of-the-art silicon packaging strategies, ultra-high-bandwidth networking design, fashionable cooling and energy administration, and algorithmic co-design of with software program. We look ahead to persevering with to advance our objective of constructing AI actual by introducing extra silicon, methods, and software program improvements into our datacenters globally.

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