How to Deploy TensorRT-LLM on NVIDIA H100 and RTX Pro 6000 GPUs

A step-by-step technical tutorial published on DEV Community outlines how to deploy large language models using NVIDIA's open-source TensorRT-LLM library on H100 and RTX Pro 6000 hardware. The guide uses Meta's Llama 3 as a working example, covering everything from container setup to serving live inference via an API. Both GPU architectures support FP8 quantization through fourth-generation Tensor Cores, which roughly halves a model's memory footprint and allows larger batch sizes. TensorRT-LLM also employs in-flight batching to keep GPU utilization continuous, reducing idle time and lowering the effective cost per million tokens generated. The tutorial targets enterprise infrastructure teams seeking to maximize throughput and reduce LLM inference costs using currently available GPU hardware.