What runs ChatGPT? Inside Microsoft's AI supercomputer | Featuring Mark Russinovich

Summary

The transcript discusses the AI supercomputer infrastructure on Azure that supports large language models like ChatGPT, with Azure CTO Mark Russinovich highlighting the advancements in AI capability driven by GPUs and cloud infrastructure. The conversation delves into the specialized hardware and software stack, the efficiency of running such resource-intensive models, and the importance of reliability and throughput. It also touches on the use of InfiniBand for networking and the potential of Confidential Computing to protect sensitive data in AI workloads. Russinovich emphasizes Azure's ability to handle a wide range of workloads and the ongoing innovation to maintain its position as a leading platform for AI and HPC.

Takeaways

• 🚀 Azure's AI supercomputer infrastructure supports large language models (LLMs) with up to 530 billion parameters, such as Microsoft's Megatron-Turing model.
• 💡 The AI infrastructure has been significantly accelerated by the rise of GPUs and cloud scale infrastructure over the last decade.
• 🔧 Azure's specialized hardware and software stack is designed to efficiently run and train models of massive scale, with self-supervised learning capabilities.
• 🛠️ The infrastructure requires high reliability to handle regular failures at scale, with quick diagnostics and fixes.
• 🌐 Azure's datacenter infrastructure is equipped with state-of-the-art hardware and high-bandwidth networks for efficient GPU clustering.
• 📈 The use of InfiniBand networking and collaboration with NVIDIA has led to the creation of large-scale systems, such as the one built for OpenAI in 2020.
• 🔄 Data parallelism is a technique used to train models like GPT by running many instances on small data batches, necessitating large-scale systems.
• 📊 Project Forge, Azure's containerization and global scheduler service, introduces transparent checkpointing and high utilization levels for AI workloads.
• 🔒 Confidential Computing is crucial for AI workloads to protect sensitive data and models, ensuring end-to-end encryption in a trusted execution environment.
• 🔄 Fine-tuning large models like GPT can be made more efficient with techniques like Low Rank Adaptive (LoRA), reducing GPU requirements and checkpoint sizes.
• 🌟 Azure's AI supercomputer capabilities are accessible for a range of workloads, from small jobs to large-scale projects like autonomous driving technologies.

Q & A

What is the significance of the AI supercomputer infrastructure in Azure for running large language models like ChatGPT?

The AI supercomputer infrastructure in Azure is designed to support the training and inference of large language models (LLMs) at scale. It allows for the efficient running of models with hundreds of billions of parameters, enabling advancements in AI capabilities and providing the necessary computational power for services like Bing chat, GitHub Copilot, and more.

How has the rise of GPUs and cloud scale infrastructure accelerated AI capabilities over the last decade?

The rise of GPUs and cloud scale infrastructure has significantly increased the computational power available for AI, leading to a substantial acceleration in AI capabilities. This has been crucial in training larger and more complex models, such as Microsoft's Megatron-Turing natural language model with 530 billion parameters.

What is the role of specialized hardware and software stack in supporting large language models?

A specialized hardware and software stack is essential for supporting the efficient running of large language models. This includes high-bandwidth networking for GPU clustering, optimized datacenter infrastructure, and software platform enhancements that allow for performance comparable to bare metal while maintaining full manageability.

How does Azure handle the resource intensity and cost associated with running large language models?

Azure manages the resource intensity and cost by investing in state-of-the-art hardware, optimizing datacenter infrastructure, and developing software frameworks like DeepSpeed for distributed training. These efforts allow for efficient utilization of resources, minimizing failures, and enabling quick diagnosis and resolution when issues arise.

What is data parallelism and why is it necessary for training large language models?

Data parallelism is a technique where many instances of a model are trained simultaneously on small batches of data. After each batch is processed, the GPUs exchange information before proceeding to the next batch. This method is necessary for training large language models due to the vast amount of data and computational power required.

How did Azure's collaboration with NVIDIA contribute to the development of AI infrastructure?

Azure's collaboration with NVIDIA led to the delivery of purpose-built AI infrastructure within NVIDIA GPUs. This includes the new H100 VM series in Azure, which allows for high-performance networking and clustering of GPUs, enabling multi-tenant cloud compute and elastic scale for AI workloads.

What is Project Forge and how does it enhance the running of AI workloads in Azure?

Project Forge is a containerization and global scheduler service developed by Azure to run large-scale AI workloads efficiently. It introduces transparent checkpointing, allowing for the periodic saving of a model's state without code intervention, and an integrated global scheduler that pools GPU capacity from regions worldwide, ensuring high utilization and the ability to migrate paused jobs as needed.

How does Azure ensure the reliability and uninterrupted running of AI jobs?

Azure ensures reliability and uninterrupted running of AI jobs through a combination of robust hardware infrastructure, software optimizations, and services like Project Forge. These elements work together to maintain high levels of utilization, quickly resume from failures, and efficiently allocate resources across different jobs.

What is the importance of Confidential Computing in the context of AI workloads?

Confidential Computing is crucial for AI workloads as it protects sensitive data used for training and the models themselves, which can contain valuable intellectual property. It provides an end-to-end trusted execution environment, ensuring data in memory is encrypted and protected from unauthorized access, including by Azure operators.

How can organizations leverage Azure's AI Supercomputer for their own solutions?

Organizations can leverage Azure's AI Supercomputer by utilizing the optimized hardware infrastructure for their AI needs, ranging from small jobs to large-scale training and inference. They can use virtual machines, AI frameworks like DeepSpeed, and Azure Machine Learning services to build, fine-tune, and deploy models for their applications.

What are some real-world examples of how Azure's AI Supercomputer is being used?

A real-world example is Wayve, a UK-based company leading in autonomous driving technologies. They use Azure's AI Supercomputer to manage and train models on millions of hours of driving data, requiring petabytes of data handling and high-performance GPU interconnect for optimal memory utilization and low latency.