Model Serving Infrastructure: Core Control Loop and Architecture Patterns

Model serving infrastructure transforms trained models into production services by orchestrating a control loop: accept network requests, queue and schedule them, optionally batch multiple requests together, execute on hardware (Central Processing Units (CPUs), Graphics Processing Units (GPUs), or specialized accelerators), run preprocessing and postprocessing pipelines, and expose metrics for autoscaling. The real complexity lies not in setting up endpoints but in three critical areas: resource scheduling across models and hardware, memory management balancing batch sizes against device capacity, and rollout safety through versioning and traffic splitting. Three architectural patterns dominate production deployments. Framework specific servers like TensorFlow Serving or TorchServe provide tight integration with a single framework, simpler mental models, and fewer moving parts. Multi backend servers like Triton abstract the runtime layer, enabling a single control plane across TensorFlow, PyTorch, Open Neural Network Exchange (ONNX), and hardware optimized backends like TensorRT or OpenVINO. This unified approach adds powerful scheduling features like dynamic batching and model ensembles but increases configuration complexity. Custom thin servers give maximum control but require building your own scheduling, observability, and deployment mechanisms from scratch. The fundamental throughput versus latency tradeoff is governed by batching and concurrency decisions. Dynamic batching aggregates requests to improve device utilization (getting more predictions per second), while per model concurrency controls parallel instances. Preprocessing and postprocessing placement determines whether you become CPU bound or accelerator bound. Precision conversions from 32 bit floating point (FP32) to 16 bit formats (BF16, FP16) or 8 bit integers (INT8) trade small accuracy drops for significant throughput gains and cost reductions. Real world benchmarks reveal optimization importance: on a V100 GPU running ResNet50 on 32,000 images, raw TensorFlow completed in 83 to 87 seconds (368 to 386 images per second), TensorFlow Serving took 117 to 120 seconds (267 to 274 images per second), and untuned Triton required 171 to 202 seconds (158 to 187 images per second). This shows serving infrastructure overhead can reduce throughput by 30% to 50% without proper tuning.

Production Triton deployment sustained 5,000 requests per second with p95 latency 50 to 70 milliseconds and GPU utilization above 70%, using zero copy shared memory for large image payloads to gain 15% throughput improvement

Medical imaging service processing 256×256×24 voxel volumes kept resampling and morphological operations in TorchServe pipeline to maintain consistent latency, with batching limited by 16 GB GPU memory footprint rather than compute

Organizations using Triton multi backend capability deployed same models on Intel Xeon CPUs with BF16 backend (using Advanced Matrix Extensions) for moderate queries per second (QPS) services, reducing GPU spend while preserving deployment workflows