RunPod Serverless Deployment Tutorial: Deploy Your Fine-Tuned LLM with vLLM

About this lesson

In this video, we walk through how to deploy a fine-tuned large language model from Hugging Face to a RunPod Serverless endpoint using the vLLM inference engine. You’ll see how to spin up a fully managed, scalable LLM API without worrying about infrastructure, and how to interact with it using an OpenAI-compatible interface. You’ll learn how to: Create a RunPod Serverless endpoint using the vLLM template Deploy a fine-tuned Hugging Face model (merged adapters) Configure GPUs, workers, and idle timeout for cost efficiency Understand cold starts and how serverless scaling works Secure your deployment with environment variables and API keys Query your deployed LLM using Python and the OpenAI SDK Stream responses token by token and control chat templates Timestamps: 0:00 - Overview: deploying a fine-tuned LLM on RunPod 0:58 - Serverless vs pods and choosing the vLLM template 2:56 - Selecting the Hugging Face model and tokenizer 4:04 - GPU selection, workers, and idle timeout explained 6:10 - Cold starts, scaling behavior, and cost implications 8:26 - Querying the endpoint via Python (OpenAI-compatible API) 10:25 - Chat templates, streaming, and response behavior 11:45 - Securing the endpoint and cleaning up resources Watch this video if you want to deploy LLMs for demos, prototypes, internal tools, or production APIs while only paying for what you use. This video is part of the LLM Engineering and Deployment Certification Program by Ready Tensor. Enroll Now: https://app.readytensor.ai/certifications/llm-engineering-and-deployment-DAROCXlj About Ready Tensor: Ready Tensor helps AI and ML professionals build, deploy, and evaluate intelligent systems, and showcase their work through certifications, competitions, and real-world project publications. Learn more: https://www.readytensor.ai/ Like the video? Subscribe and comment with what deployment or inference topics you want to see next.

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