Scale to 0 LLM Inference: Cost Efficient Open Model Deployment on Serverless GPUs

Overview

Discover how to run Ollama on serverless GPUs that scale efficiently, including down to zero when inactive, for cost-effective open LLM deployment with full control over models and private data.

Syllabus

- **Introduction to Serverless GPU Computing** -- What is serverless computing? -- Benefits of serverless infrastructures for AI/ML -- Understanding GPU utilization and scaling - **Overview of Ollama and LLM Deployment** -- What is Ollama? -- Introduction to Large Language Models (LLMs) -- Importance of model and data privacy - **Setting Up a Serverless Environment** -- Selecting a cloud provider -- Setting up serverless GPU resources -- Configuring security and access permissions - **Deploying LLMs on Serverless GPUs** -- Installing and configuring Ollama -- Model selection and preparation -- Packaging and deploying an LLM - **Cost Optimization Strategies** -- Scaling to zero: Understanding and leveraging scale-down strategies -- Monitoring usage and costs -- Implementing usage-based triggers - **Maintaining Model and Data Privacy** -- Ensuring data remains private and secure -- Methods for encrypting communications -- GDPR and other privacy compliance considerations - **Performance Optimization** -- Techniques for improving inference speed -- Balancing cost and performance -- Case studies of successful deployment solutions - **Troubleshooting and Support** -- Common issues and solutions -- Accessing community and support resources -- Future-proofing and maintaining systems - **Capstone Project** -- Deploy a sample LLM using serverless GPUs -- Presentation and evaluation of deployment strategy - **Course Conclusion and Future Directions** -- Recap of key concepts -- Emerging trends in AI deployment -- Opportunities for further learning and exploration

Taught by

Tags