Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // MLOps Podcast

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MLOps podcast #228 with Simon Karasik, Machine Learning Engineer at Nebius AI, Handling Multi-Terabyte LLM Checkpoints.
// Abstract
The talk provides a gentle introduction to the topic of LLM checkpointing: why is it hard, how big are the checkpoints. It covers various tips and tricks for saving and loading multi-terabyte checkpoints, as well as the selection of cloud storage options for checkpointing.
// Bio
Full-stack Machine Learning Engineer, currently working on infrastructure for LLM training, with previous experience in ML for Ads, Speech, and Tax.
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Timestamps:
[00:00] Simon preferred beverage
[01:23] Takeaways
[04:22] Simon's tech background
[08:42] Zombie models garbage collection
[10:52] The road to LLMs
[15:09] Trained models Simon worked on
[16:26] LLM Checkpoints
[20:36] Confidence in AI Training
[22:07] Different Checkpoints
[25:06] Checkpoint parts
[29:05] Slurm vs Kubernetes
[30:43] Storage choices lessons
[36:02] Paramount components for setup
[37:13] Argo workflows
[39:49] Kubernetes node troubleshooting
[42:35] Cloud virtual machines have pre-installed mentoring
[45:41] Fine-tuning
[48:16] Storage, networking, and complexity in network design
[50:56] Start simple before advanced; consider model needs.
[53:58] Join us at our first in-person conference on June 25 all about AI Quality