[Maia 200] The Maia 200 is Microsoft’s Azure second-gen inference powerhouse. Maia against Ironwood. скачать в хорошем качестве

[Maia 200] The Maia 200 is Microsoft’s Azure second-gen inference powerhouse. Maia against Ironwood.

Welcome to the era of the "Inference Wars." While NVIDIA usually hogs the spotlight, the real battle for the future of AI economics is happening in the custom silicon labs of Redmond and Mountain View. Here is the essential breakdown for your podcast on the Microsoft Maia 200 and its direct rival, Google Ironwood (TPU v7). 🎙️ The Headliner: Microsoft Maia 200 Announced today (January 26, 2026), the Maia 200 is Microsoft’s second-generation "inference powerhouse." It isn't just a chip; it’s a declaration of independence from high GPU margins. Key Specifications Fabrication: TSMC 3-nanometer process (140 billion transistors). Raw Compute: 10+ PetaFLOPS at FP4 precision; 5+ PetaFLOPS at FP8. Memory: 216GB of HBM3e with 7 TB/s bandwidth. The "Secret Sauce": 272MB of on-die SRAM. This acts as a hyper-fast scratchpad, keeping model weights local to the "Tile Tensor Units" (TTUs) and avoiding the energy tax of moving data back and forth to main memory. Networking: A two-tier scale-up network using standard Ethernet (Maia AI Transport), allowing clusters of up to 6,144 accelerators. 🌩️ The Challenger: Google Ironwood (TPU v7) Google has been in the custom silicon game for a decade. Ironwood is the first TPU explicitly "re-architected" for the inference-heavy world of Gemini and massive-scale agentic workflows. Key Specifications Focus: Massive scale-out efficiency. Google treats the entire "TPU Pod" as a single cohesive supercomputer. Memory: 192GB of HBM3e with 7.4 TB/s bandwidth. Networking: Uses Google’s proprietary Optical Circuit Switching (OCS) and Inter-Chip Interconnect (ICI) at 1.2 Tbps. It scales to a staggering 9,216 chips in a single "Superpod." Cooling: Advanced liquid cooling is standard, allowing it to sustain peak performance longer than traditional air-cooled racks.