Model Serving

Model serving transforms a trained model artifact into a production service. At its simplest, it is a web server that accepts an input payload, runs inference through the model, and returns a prediction. In practice, production serving involves a cascade of engineering decisions: which hardware (CPU, GPU, TPU) to target, how to batch concurrent requests for throughput efficiency, how to manage model loading and memory, and how to route traffic across multiple instances as load fluctuates.

The serving stack has evolved significantly for LLMs. Techniques like continuous batching, KV-cache management, and speculative decoding are now essential for serving large language models at acceptable cost. Purpose-built LLM serving engines (vLLM, TGI, TensorRT-LLM) deliver 3–10x higher throughput than naive inference servers by exploiting these optimizations — the difference between a per-token cost that is economical at scale and one that makes the business case impossible.

Enterprise serving architectures must address four dimensions: **latency** (P95/P99 targets for user-facing applications), **throughput** (requests per second at peak load), **availability** (multi-region deployment, failover, SLA), and **cost** (GPU utilization rates, spot vs. on-demand compute, model quantization). Each application type — real-time chat, batch document processing, embedded API — has a different optimal point on that surface.