Having applied quantization techniques and evaluated their impact, the next step is preparing these models for operational use. This chapter addresses the practical aspects of deploying quantized Large Language Models (LLMs) efficiently.

You will examine optimization methods that complement quantization, such as specialized kernel usage and efficient attention mechanisms. We will guide you through selecting and utilizing appropriate deployment frameworks tailored for quantized models, including Text Generation Inference (TGI), vLLM, NVIDIA TensorRT-LLM, and ONNX Runtime. The chapter also covers hardware-specific tuning, particularly for GPUs, along with essential strategies for containerization, scaling, and monitoring these optimized models in production environments. By the end, you will be equipped to choose the right tools and implement effective deployment pipelines for your quantized LLMs.