If you're running LLMs in production and your serving setup is one pool of GPUs doing both prompt processing and token generation, you're leaving significant throughput on the table. Not in a theoretical, "someday we'll optimize" way. Right now, in mid-2026, every major serving framework supports the fix and teams like Baseten are reporting 50% lower time-to-first-token and 61% more requests per second after switching. The pattern is called disaggregated prefill/decode serving. It's been production-ready since NVIDIA Dynamo 1.0 went GA in March, and it's now supported natively by vLLM, SGLang, TensorRT-LLM, and LMDeploy. If you haven't looked at it yet, this is the week. Why Prefill and Decode Don't Belong on the Same GPU LLM inference has two fundamentally different phases. Prefill is where the model processes your input prompt. If you send a 50,000-token context, the model has to attend over all of it to generate the first token. This is com...