Serving Multiple LoRA Adapters on a Single GPU - Implementation and Management Guide

Learn how to efficiently serve multiple LoRA adapters on a single GPU in this comprehensive 57-minute technical tutorial. Master the theory and practical implementation of Low Rank Adapters (LoRA) for inference, starting with fundamental concepts and progressing to advanced implementations. Explore GPU VRAM management, adapter storage solutions, and hands-on demonstrations of both basic LoRaX and advanced vLLM implementations. Gain practical experience setting up environments, building proxy servers, implementing Redis for adapter management, and configuring SSH connections for Runpod. Follow along with detailed code examples, step-by-step server deployment instructions, and real-world testing scenarios. Complete the learning journey with a demonstration of the FineTuneHost.com service and access to comprehensive resources for further development.

Syllabus

- Introduction to serving multiple models on GPU
- Overview of using LoRA adapters as clip-ons
- Video structure overview
- Theory of LoRA for inference
- Explanation of LoRA Low Rank Adapters
- Benefits of using LoRA for training
- Practical implementation of LoRA loading
- GPU VRAM and model loading explanation
- Managing adapter downloads and storage
- Basic LoRaX Implementation
- Setting up the environment
- Running inference with LoRaX
- Setting up SSH connection for Runpod
- Advanced vLLM Implementation
- Building the proxy server
- Redis implementation for adapter management
- Starting the server
- Testing the service
- FineTuneHost.com service demonstration
- Conclusion and resource overview