The disaggregation of LLM inference is not a clever optimization trick. It is the clearest path we have to cutting inference costs by 2 to 4 times, and the fact that most ML teams have not adopted it yet says less about its complexity and more about the gravitational pull of familiar architectures. The core insight is refreshingly simple: prefill is compute-bound, decode is memory-bound, and no single GPU is built to excel at both. Forcing one piece of hardware to serve two opposing workloads is how you end up paying for peak capacity you rarely use and tolerating latency you do not have to accept.
What this means for you, the team shipping models into production, is a license to stop treating the GPU as a monolithic workhorse. You do not need a bigger hammer. You need to separate the two phases of inference and match each to the hardware that does the job without apology. Prefill wants raw compute, the kind of parallel throughput that fills a chip with dense matrix operations. Decode wants memory bandwidth, the ability to feed a token through a model with minimal latency and maximal reuse of what is already in cache. When you run both on the same GPU, you are making a compromise that benefits neither phase. You are paying for compute you do not use during decode and memory you do not use during prefill.
The practical takeaway is not that you must rip out your entire stack tomorrow. It is that the next time you profile your inference workload, you should ask a different question. Instead of asking how many tokens per second your GPU can produce, ask what fraction of the GPU's resources are actually being utilized during each phase. Disaggregation is not about adding complexity for its own sake. It is about giving each phase the hardware it needs to run efficiently, which in turn lowers your cost per token and improves your latency profile. The teams that figure this out will not just save money. They will build systems that scale without requiring a second mortgage on the cloud bill.
Most ML teams have not adopted this architecture yet, and that is exactly why the opportunity is still open. The window for being early is closing, but it is not shut. Start by measuring the utilization gap between your prefill and decode phases. If the numbers show you are leaving performance on the table, the disaggregation play is not a gamble. It is the next logical step. The hardware exists. The logic is sound. The only missing piece is the decision to stop forcing one GPU to do two jobs and start matching the phase to the machine that does it best.