Deploying Large Language Models (LLMs) for inference is a complex yet rewarding process that requires balancing performance, cost, and scalability. Optimizing and sizing LLM inference systems involves understanding tradeoffs, selecting the right tools, and leveraging NVIDIA’s advanced technologies like TensorRT-LLM, Triton Inference Server, and NVIDIA Inference Microservices (NIM). This guide explores the key techniques and strategies for efficient LLM deployment.