Title: Discussion 11 — MIG + DRA vs. "One Pod, One GPU"
Course: AIINFRA 200: Production Inference Serving & GPU Orchestration > Section: Week 11 — Dynamic Resource Allocation and MIG Partitioning

💬 Discussion 11 — MIG + DRA vs. "One Pod, One GPU" (20 pts)

How does MIG partitioning combined with Dynamic Resource Allocation enable better GPU utilization for heterogeneous workload mixes, compared to the traditional "one pod = one GPU" allocation model? In your post, consider a concrete mixed workload scenario (for example, a small batch-inference job, a mid-size fine-tuning job, and a large evaluation job all competing for GPU capacity) and explain specifically where the old device-plugin model would waste capacity and how MIG + DRA would right-size it instead. Feel free to reference what you built in Lab 11. Respond to at least two classmates — ideally engaging with a scenario or partitioning choice different from your own — and discuss any tradeoffs (like scheduling delay or fragmentation) they may have missed.