Model Serving

What is Model Serving?

Model serving deploys trained machine learning models as production services that accept inference requests and return predictions with low latency and high availability. It bridges the gap between trained model artifacts and real-time applications that depend on predictions — handling request batching, model loading, hardware acceleration, scaling, and monitoring. Frameworks like vLLM, TensorRT-LLM, Triton Inference Server, and BentoML specialize in serving models efficiently at production scale.

How does Model Serving work?

Model serving systems load trained weights onto inference hardware (GPUs, TPUs, or CPUs) and expose prediction endpoints via HTTP/gRPC APIs. When requests arrive, the serving system tokenizes inputs, runs forward passes through the model, decodes outputs, and returns responses within latency budgets.

Request batching combines multiple incoming requests into a single forward pass, dramatically improving GPU utilization. Dynamic batching collects requests over a time window (typically 5-50ms) and processes them together. Continuous batching interleaves requests at the iteration level for autoregressive models, preventing short requests from waiting behind long generations.

Model parallelism distributes large models across multiple GPUs when they exceed single-device memory. Tensor parallelism splits individual layers across devices, while pipeline parallelism places different layers on different devices. Serving systems manage inter-device communication transparently.

Autoscaling adjusts replica count based on queue depth, latency percentiles, or GPU utilization metrics. Scale-to-zero deployments eliminate costs during idle periods, while pre-warmed instances handle traffic spikes without cold-start latency.

Why does Model Serving matter?

The gap between model training and production deployment kills most ML projects. A model that runs in a notebook at 10 requests per minute needs to serve 10,000 per minute in production with p99 latency under 200ms. Model serving infrastructure handles this 1000x scaling challenge while maintaining reliability and cost efficiency.

Best practices for Model Serving

• Implement request batching with tuned batch sizes and timeout windows to maximize GPU utilization without excessive latency
• Use quantized model formats (INT8, FP8, INT4) to increase throughput and reduce memory requirements in production
• Deploy health checks and graceful shutdown to prevent dropped requests during scaling events and deployments
• Monitor serving metrics including tokens-per-second, time-to-first-token, and queue wait time for capacity planning
• Implement model versioning with traffic splitting to enable safe A/B testing of new model versions in production