Serving AI models on a single GPU for multi tenant workload sounds challenging till you partition a GPU correctly.

This talk is a deep technical exploration of running AI inference workloads on modern GPUs across Hopper and Blackwell using Multi-Instance GPU (MIG) isolation.

We'll explore:

1. The multi-tenant problem: MIG vs other GPU slicing methods.
2. MIG Fundamentals: Key concepts, working and support.
3. Managing MIG instances: creation, configuration, monitoring and deletion.
4. Identifying right approaches based on your workload.
5. Common issues and failures

Whether you're building a multi-tenant inference platform, optimizing GPU utilization for your team, or exploring how to serve AI models cost-effectively, this talk provides practical configurations for your AI workloads.