Good question. Let’s break it down carefully.

When you hear about a single LLM instance serving multiple clients at the same time, it usually works like this: • The LLM instance is stateless: Each client sends a request (prompt + settings), the model processes that one request independently, and returns the response. The LLM doesn’t “remember” between requests unless you explicitly include conversation history in the prompt. • Concurrency is handled by infrastructure: Even though the LLM is “one model,” it can handle many incoming requests because the backend (server) wraps the model with techniques like: • Asynchronous request handling (e.g., using async/await patterns) • Batching: multiple prompts are packed together into a single forward pass through the model (very common in high-traffic servers) • Parallelism: the server could have multiple workers/replicas of the model (copies or shared GPUs) running side-by-side. • Queueing: if too many clients at once, requests are queued and processed in order. • Memory isolation: Each request is kept separate in memory. No client’s data leaks into another client’s conversation unless you (the app developer) accidentally introduce a bug.

So:

It’s not that one model is “locked” into serving only one person at a time. It’s more like the model is a very fast function being called many times in parallel.

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