Tuning and optimizing modern Large Language Model (LLM) serving is challenging due to the myriad of interacting deployment choices, which influence bottlenecks across the entire stack.
Modern LLM serving is difficult to tune because each deployment involves a complex stack of interacting choices. These choices include the model backend, tensor-parallel shape, prefill/decode split, worker counts, scheduler settings, routing policy, KV cache behavior, autoscaling thresholds, and overall topology. These parameters interact across layers, meaning a local optimization can inadvertently shift the performance bottleneck elsewhere in the system, posing a significant challenge for deploying larger models.