This post provides a guide on deploying large language models into production using TensorRT-LLM. It explains the benefits of using TensorRT-LLM and how to compile a model using this framework. It also introduces Truss as a tool for deploying compiled models and highlights its features. The post concludes with performance benchmarks and the author's recommendation to use TensorRT-LLM for state-of-the-art inference.
Table of contents
Hands-On Python TutorialStep 1: Compiling the modelMistral 7B Compiler Google ColaboratoryStep 2: Deploying the compiled modelGitHub - htrivedi99/mistral-7b-tensorrt-llm-trussDeploying the model in GKEPerformance BenchmarksConclusionEnjoyed This Story?Get an email whenever Het Trivedi publishes.ImagesShare this post