If you have 100 different customers, each with a custom-tuned LoRA adapter, do you need 100 different GPU clusters? How would you serve them efficiently on one cluster?

When would you choose to run a model locally on a user's device (using WebLLM or ONNX) instead of the cloud? Focus on privacy and cost.

Can you use "Spot" or "Preemptible" GPU instances for real-time inference? What happens to the user's request if the cloud provider reclaims the GPU mid-generation?

You are running a high-volume AI application. You notice that 15% of your costs come from 'Refinement Loops' where the model has to correct its own initial mistakes. How do you architect a 'Data Flywheel' to reduce these costs over time, and how do you handle the 'Data Contamination' risk of training a model on its own synthetic outputs?