E001 - AI Insights with Ben Whorwood :: Local LLM Deployment

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In the inaugural episode of AI Insights, Enterprise Architect in Residence Ben Whorwood takes viewers on an exciting journey into the world of running large language models (LLMs) locally. This eye-opening session provides practical insights for both technical and non-technical founders alike.

Ben kicks off by introducing Retrieval Augmented Generation (RAG), a technique developed by Meta researchers that allows users to provide additional context to LLMs at runtime. He then showcases Ollama, a versatile tool for deploying LLMs on personal computers, compatible with Mac, Linux, and Windows.

The episode delves into various Llama models available through Ollama, highlighting the trade-offs between model size, performance, and hardware requirements. Ben demonstrates how even laptops with modest specifications can run smaller models, opening up possibilities for local AI experimentation.

Key takeaways from the episode include:

1. The advantages of running LLMs locally, including enhanced privacy and consistent performance
2. The importance of temperature settings in controlling response variability
3. Practical applications of local LLMs, such as building AI chatbots and enhancing search capabilities

Ben shares a recent experience where Llama outperformed ChatGPT in generating non-functional requirements for a proposal, emphasizing the potential of these models.

As the series progresses, viewers can look forward to deeper dives into RAG implementation, exploration of AI startups, and insights into the rapidly evolving AI landscape.

This first episode of AI Insights with Ben Whorwood provides a compelling introduction to local LLM deployment, empowering viewers to explore AI technologies hands-on and stay ahead in the fast-paced world of artificial intelligence.

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