Microsoft has built the world’s largest cloud-based AI supercomputer that is already exponentially bigger than it was just 6 months ago, paving the way for a future with agentic systems.
For example, its AI infrastructure is capable of training and inferencing the most sophisticated large language models like GPT-4o at massive scale on Azure. In parallel, Microsoft is also developing some of the most compact small language models with Phi-3, capable of running offline on your mobile phone.
Watch Azure CTO and Microsoft Technical Fellow Mark Russinovich demonstrate this hands-on and go into the mechanics of how Microsoft is able to optimize and deliver performance with its AI infrastructure to run AI workloads of any size efficiently on a global scale.
This includes a look at: how it designs its AI systems to take a modular and scalable approach to running a diverse set of hardware including the latest GPUs from industry leaders as well as Microsoft’s own silicon innovations; the work to develop a common interoperability layer for GPUs and AI accelerators, and its work to develop its own state-of-the-art AI-optimized hardware and software architecture to run its own commercial services like Microsoft Copilot and more.
► QUICK LINKS:
00:00 - AI Supercomputer
01:51 - Azure optimized for inference
02:41 - Small Language Models (SLMs)
03:31 - Phi-3 family of SLMs
05:03 - How to choose between SLM & LLM
06:04 - Large Language Models (LLMs)
07:47 - Our work with Maia
08:52 - Liquid cooled system for AI workloads
09:48 - Sustainability commitments
10:15 - Move between GPUs without rewriting code or building custom kernels.
11:22 - Run the same underlying models and code on Maia silicon
12:30 - Swap LLMs or specialized models with others.
13:38 - Fine-tune an LLM
14:15 - Wrap up
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GPT-4o is the large language model used behind Apple Intelligence and updates to Siri.
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