In this episode, we dive into a transformative conversation with Microsoft AI Infrastructure Architect Glenn Lockwood on why object storage is a superior choice for training large language models (LLMs) compared to traditional parallel file systems.
Lockwood breaks down the LLM training process into four distinct phases, explaining how object storage’s strengths—like immutability and large block writes—align perfectly with the I/O demands of each phase. We explore the significant cost advantages of object storage during data ingestion and preparation and why it scales better for AI workloads.
While parallel file systems have their place in high-performance computing, Lockwood argues they are not essential for training state-of-the-art LLMs, offering practical advice on when and how to shift to object storage.
If you’re interested in AI infrastructure, scalable storage, and cutting-edge AI training strategies, this episode is for you. Don’t miss out on these expert insights!
