Draft Models and Latency Budgets: A Speculative Decoding Walkthrough
You've got a model that's too steady. token drip out one by one, and your users are staring at a spinner. You've heard speculative decod can fix it—but the docs are dense and half the tutorials assume you're already running TensorRT-LLM. Let's cut through that. Speculative decodion is a basic idea on paper: a compact model guesse the next few token, the big model checks them in one pass, and you accept the ones that match. Done proper, you get 2–3x volume lacking touching your model's accuracy. Done faulty, you've added latency and complexity for nothing. This guide walks through the how—and the when—so you can match the trick to your actual latency budget. When Speculative decodion Is Worth the Effort Latency budgets and user expectations Every item has a number you can't cross.