A trained model artifact is not a production service. The process of deploying a model for inference introduces a distinct set of engineering challenges centered on performance and efficiency. While training prioritizes throughput over long jobs, inference services often must meet strict service-level objectives (SLOs) for single-request latency, such as a 99th percentile response time of $p99 < 100ms$. Meeting these goals requires specific optimizations at both the model and the infrastructure levels.

This chapter covers the methods for engineering and deploying high-performance inference systems. We will start by examining the architectural trade-offs between low latency and high throughput. You will then learn to apply post-training optimizations directly to your models, including:

• Model Compilation: Using runtimes like TensorRT and ONNX to apply graph optimizations, kernel fusion, and hardware-specific tuning.
• Quantization: Reducing a model's computational and memory footprint by converting weights and activations from 32-bit floating-point to lower-precision formats like INT8.

With an optimized model, we will move to deployment using the NVIDIA Triton Inference Server. You will see how to manage multiple models, configure dynamic batching to improve GPU utilization, and implement safe rollout strategies like A/B testing and canary deployments for new model versions.