This blog post provides a comprehensive guide on deploying Large Language Models (LLMs) serverlessly using Modal Labs, focusing on Mistral-7B-instruct by Mistral AI. It outlines the process of serverless deployment, emphasizing the cost-effectiveness of this approach as charges are based on computational usage rather than fixed resources, and highlights the potential drawback of cold starts when servers reactivate after idling. The tutorial explains how to set up and configure a serverless deployment using Modal's Python interface, detailing the creation of necessary files like constants.py for configurations, engine.py for the inference engine, and server.py for the REST endpoint. The post elaborates on using GPU configurations for model deployment, leveraging a Docker container environment, and implementing Modal stubs for efficient resource management. It concludes by describing the deployment process using the Modal CLI, suggesting that serverless deployment is ideal for varying usage patterns and promising future discussions on alternative serverless providers like Beam Cloud and Runpod.