The generative AI boom has reshaped how tech giants approach infrastructure—and Microsoft is now all-in on building its own AI chips. Historically dependent on NVIDIA GPUs to power services like GitHub Copilot and Azure OpenAI Service, Microsoft has shifted course: it’s developing custom AI accelerators and server CPUs to gain more control over its cloud performance, costs, and supply chains. This strategy, still in its early phases, is Microsoft’s answer to rising competition—and exploding demand.

Maia and Cobalt: Microsoft’s First AI Chips

In late 2023, Microsoft unveiled two custom chips:

• Azure Maia 100: A purpose-built AI accelerator designed to handle massive training and inference workloads for large models. Co-developed with feedback from OpenAI, it uses 5nm silicon, packs 105 billion transistors, and integrates tightly with Azure’s infrastructure, including custom racks, high-bandwidth Ethernet, and liquid cooling.
• Azure Cobalt 100: A 128-core Arm-based CPU optimized for general-purpose cloud workloads. It prioritizes energy efficiency and is already running in production datacenters.

Together, these chips mark Microsoft’s push toward vertical integration—bringing silicon, software, and systems design under one roof.

Systems-Level Optimization

Microsoft’s approach isn’t just about the chips. It’s about the entire stack:

• Custom racks and liquid cooling accommodate Maia’s power density.
• Software co-design ensures Maia works seamlessly with PyTorch, ONNX, and Triton.
• Azure Boost and FPGA offloading free up CPUs for high-priority tasks.

This full-stack strategy echoes what Apple did with its M-series chips—only at datacenter scale.

Partnering to Compete

Even as it builds its own chips, Microsoft continues to partner widely:

• NVIDIA and AMD: Azure offers the latest GPUs, including H100s and AMD’s MI300X, to meet diverse customer needs.
• AMD Collaboration: Microsoft engineers have supported AMD’s AI roadmap; AMD may also contribute IP to future custom chip efforts.
• OpenAI: Microsoft and OpenAI co-designed infrastructure, shaping Maia’s capabilities.
• Oracle Cloud: To meet surging demand, Microsoft partnered with Oracle to run OpenAI workloads using Azure’s software stack.

This hybrid strategy ensures flexibility while reducing over-reliance on any single vendor.

Comparing the Field: Microsoft vs. the AI Chip Titans

🔵 NVIDIA: The Incumbent

• Strength: World-class GPUs + CUDA software stack
• Moat: Deep ecosystem lock-in
• Challenge for Microsoft: Replace or reduce dependence on NVIDIA’s high-cost GPUs without sacrificing performance

🔵 Google: The Early Mover

• Strength: TPUs optimized for internal workloads (Search, YouTube, etc.)
• Edge: Decade-long investment in custom silicon
• Comparison: Microsoft is replicating this internal+cloud strategy but with a more open developer stack

🔵 Amazon AWS: The Operational Benchmark

• Strength: Inferentia (inference) and Trainium (training) chips now in second/third generation
• Edge: Claimed cost/performance leadership; chips widely adopted in AWS
• Microsoft Match: Maia and Cobalt mirror this CPU/AI accelerator strategy, but trail by a generation

🔵 Oracle: The Systems Integrator

• Strength: No custom chips; instead optimized GPU clusters with ultra-low latency networking
• Edge: AI startups like MosaicML and even OpenAI use Oracle for speed and availability
• Role: Strategic partner to Microsoft in offloading compute during capacity crunches

Why It Matters

For Microsoft, owning the AI chip layer is about more than performance. It’s about reducing costs, ensuring supply, and controlling its destiny as AI workloads redefine cloud infrastructure. As cloud AI shifts from GPU scarcity to diversified silicon ecosystems, Microsoft’s play could reshape Azure’s economics—and the broader AI infrastructure market.

With Maia and Cobalt in production and Gen2 designs already underway, Microsoft has officially entered the silicon race. The era of hyperscalers building their own AI chips is no longer optional—it’s table stakes.