AI Infrastructure · 8 min
Designing production-ready LLM inference
Inference is an infrastructure program: serving, scheduling, latency budgets, and cost controls — not a model checkpoint.
Teams often treat LLM inference as a model problem. In production it is an infrastructure program: serving topology, GPU scheduling, networking, observability, and a cost model that survives real traffic.
A production-ready design starts with the request path. Who calls the model, with what concurrency, and what is the p95 latency the product can tolerate? Those constraints decide whether you need a dedicated inference cluster, a batch pipeline, or both — long before you argue about quantization.
GPU utilization is the control variable most programs ignore. Idle accelerators look like capacity; they are usually a scheduling and batching failure. Token throughput only matters once the cluster is actually busy on useful work.
Observability has to cover the whole path: queue depth, batch size, time-to-first-token, tokens per second, GPU duty cycle, and cost per successful request. If those signals are missing, you cannot tell a model regression from a networking stall.
The consultancy posture is to freeze the program shape early: serving pattern, isolation boundaries, autoscaling policy, and a review cadence for cost. The model will change. The infrastructure program should not have to be redesigned every sprint.