The Split Personality of AI Inference
How LLM-D Parallel Runs Are Rewriting the Rules of Model Inference Copyright: Sanjay Basu When One Brain Isn’t Enough What if the secret to making AI faster wasn’t building bigger machines, but teaching it to think with two minds at once? For anyone who’s ever typed a prompt into ChatGPT and watched those little dots dance across the screen, there’s an invisible orchestra playing behind the curtain. Large language models don’t just materialize answers from thin air. They’re running a two-act play every single time: first, they digest your question (prefill), and then they generate your answer, token by token (decode). Traditionally, these two acts happened on the same stage, using the same resources. And like any double-booked theater, chaos ensued. Enter LLM-D, the distributed inference framework that said, “What if we gave each act its own theater?” The result? A system that can serve AI models faster, cheaper, and more reliably by splitting the inference process into spec...
