Microsoft Maia 200 Explained: The New AI Inference Chip Challenging Nvidia (FP4/FP8, HBM3e, Azure) скачать в хорошем качестве

Microsoft Maia 200 Explained: The New AI Inference Chip Challenging Nvidia (FP4/FP8, HBM3e, Azure)

#AI #Microsoft #Azure #Nvidia #MachineLearning #DataCenter #GPUs #Semiconductors #CloudComputing #TechNews Microsoft just unveiled Maia 200—its custom AI inference accelerator—and the real story isn’t “a new chip.” It’s a full-stack datacenter play to make the next token cheaper than anyone else. In this deep dive, we break down what AI inference actually is (and why it becomes the permanent bill once your product scales), what Microsoft claims Maia 200 can do (TSMC \(3\text{ nm}\), FP4/FP8 tensor cores, \(216\text{ GB}\) HBM3e at \(7\text{ TB/s}\), \(750\text{ W}\) TDP), and why memory bandwidth matters as much as raw FLOPS—think supercar engine, tiny fuel line. Then comes the twist: Maia 200 is paired with a two-tier scale-up network built on standard Ethernet, with a custom transport layer and integrated NIC—because at hyperscale, networking is a performance tax you can’t ignore. We also cover where Microsoft says it’s deployed, how the Maia SDK aims to reduce porting pain (PyTorch + Triton), what Microsoft says it will run (including “latest GPT-5.2 models”), and the risks: fragmentation, platform lock-in, and overreliance as inference gets cheaper. Notes on claims: performance and “X times faster” comparisons are discussed as Microsoft’s statements unless independently benchmarked. What should I cover next: Google’s Ironwood TPU strategy, Amazon’s Trainium roadmap, or a plain-English breakdown of FP4 vs FP8? #Microsoft #Maia200 #Azure #AIInference #LLM #AIHardware #DataCenter #TokenEconomics #FP4 #FP8 #HBM3e #Ethernet #CloudComputing #Copilot #PyTorch #Triton #TPU #Trainium #Nvidia Disclaimer: This video is for informational and educational purposes only. All product names, logos, and brands are property of their respective owners.