LLMOps and GPU Inference Acceleration with TensorRT-LLM

Serving large language models in production environments demands efficient memory usage, low latency, and cost-effective GPU utilization. This text-based course guides you through foundational LLMOps practices and the mechanics of GPU acceleration using TensorRT-LLM. What you'll learn: Understand core LLMOps concepts, GPU memory architecture, and inference bottlenecks; Apply model optimization strategies including weight quantization such as FP8, INT8, and INT4; Configure TensorRT-LLM to build and compile high-performance engine files; Implement in-flight batching and key-value cache optimizations to maximize request throughput; Deploy compiled models with production-grade inference server setups; Measure and monitor critical metrics like time-to-first-token and overall latency. The course begins with foundational terminology and architecture concepts before progressing into practical configuration files, compilation workflows, and serving architectures. Designed for developers, data practitioners, and engineers starting out in LLMOps, with no prior deep GPU-optimization background required. Begin reading to optimize your language model deployments today.