As large language models (LLMs) become more accessible, running them locally unlocks exciting opportunities for developers, engineers, and privacy-focused users. Why rely on costly cloud AI services that share your data when you could deploy your own models tailored to your needs? In this session, we’ll dive into the advantages of local LLM deployment, from selecting the right open source model to optimizing performance on consumer hardware and integrating with your unique data.
Let’s explore the journey to your own local stack for AI, and cover the important technical details such as model quantization, API integrations with IDE code assistants, and advanced methods like Retrieval-Augmented Generation (RAG) to connect your LLM to private data sources. Don’t miss out on the fun live demos that prove the bright future of open source AI is already here!
Let’s explore the journey to your own local stack for AI, and cover the important technical details such as model quantization, API integrations with IDE code assistants, and advanced methods like Retrieval-Augmented Generation (RAG) to connect your LLM to private data sources. Don’t miss out on the fun live demos that prove the bright future of open source AI is already here!
Cedric Clyburn
Red Hat
Cedric Clyburn (@cedricclyburn), Senior Developer Advocate at Red Hat, is an enthusiastic software technologist with a background in Kubernetes, DevOps, and container tools. He has experience speaking and organizing conferences including DevNexus, WeAreDevelopers, The Linux Foundation, KCD NYC, and more. Cedric loves all things open-source, and works to make developer's lives easier! Based out of New York.