Title: Discussion 15 — Simple Today vs. Production-Ready at Scale
Course: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization > Section: Week 15 — Serving Adapted and Quantized Models — Handoff to AIINFRA 200

💬 Discussion 15 — Simple Today vs. Production-Ready at Scale (20 pts)

You've built a fine-tuned and quantized model. Now imagine two different asks land on your desk on the same day:

- "Can you get this in front of a few internal users tomorrow, just to try it out?"
- "Marketing wants to put this behind our public product at scale next quarter — what does that take?"

Post a response (200+ words) that answers:

1. What is the simplest way you would put your model in front of users tomorrow? Name the specific tool(s) and why they fit a "get something working fast" constraint.
2. What is the most production-ready way you would serve this model at scale? Name the specific tool(s) and what scaling properties (multi-adapter serving, throughput, GPU utilization) make them the right choice.
3. What concretely would make you choose one path over the other — is it request volume, number of adapter variants, latency requirements, team GPU budget, something else?

Then respond to at least two classmates. In your responses, push on their reasoning a bit: did they account for the adapter-compatibility and silent-failure risks from this week's lecture? Would their "tomorrow" choice actually survive being quietly scaled up without anyone revisiting the decision?