This tutorial discusses the deployment of a Llama 3 model on AWS Trn1/Inf2 instances, which offers improved price-performance, latency, and availability compared to traditional methods. The author highlights the benefits of using specialized frameworks such as vLLM and batching to improve inference speed and throughput, as well as semantic caching and MIG instances to further optimize performance. The tutorial provides a step-by-step guide on how to deploy the Llama 3 model on Inf2 nodes, including setting up a Cerebrium account, creating a starter project, and configuring the `cerebrium.toml` file. The results show that the deployment of the Llama 3 model on Inf2 instances offers significant improvements in throughput and latency, as well as cost savings compared to traditional methods. Additionally, the tutorial highlights the flexibility of Cerebrium's platform, which allows engineers to run applications on hardware that best suits their use case.