Here's a 1-paragraph summary of the text, covering key points: Deploying ML models on top of a powerful CPU can be an efficient and cost-effective way to serve machine learning models. Railway provides a great platform for deploying ML models using NVIDIA Triton Inference Server, which is supported by many ML platforms. The model repository feature in Triton allows for easy management of multiple models, including dynamic addition and removal, making it a solid option for serving ML models. By leveraging Railway's persistence features and the MinIO object storage system, users can easily deploy and manage their models, taking advantage of the scalability and flexibility offered by this platform.