Deploying and optimizing large language models (LLMs) for high-performance, cost-effective serving can be an overwhelming engineering problem. The ideal configuration for any given workload (such as hardware, parallelism, and prefill/decode split) resides in a massive, multi-dimensional search space that is impossible to explore manually or through exhaustive testing. AIConfigurator, an open source tool that simplifies the NVIDIA Dynamo AI serving stack, is intended to cut through this complexity and get you to an optimal deployment in minutes. The core benefit of AIConfigurator is that you don’t need to run every possible configuration on real hardware to predict which one will perform best. Instead, it decomposes LLM inference into its constituent operations and measures each one in isolation on the target GPU. AIConfigurator can then reassemble those measurements to estimate the end-to-end performance of any configuration, all without occupying a single GPU-hour at search time.…