In a high-concurrency environment, how does PagedAttention prevent the GPU from running out of memory (OOM) when multiple users are chatting simultaneously?

How do you monitor for "Concept Drift" in an LLM application? If the model's output starts getting shorter over time, is that a deployment failure or a data failure?

When is it more cost-effective to use a "Pay-per-token" API (like OpenAI) versus hosting your own model on a dedicated cloud instance (like an AWS g5 instance)?

If your goal is to process 1,000,000 documents as fast as possible (offline), how does your deployment strategy differ from a real-time chatbot (online)?