#31 - Open vs. Closed LLMs: A Strategic Guide

The Data Coffee Break Podcast

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Ever wondered how cycling from London to Paris could inspire an in-depth discussion about AI? Join us as Marc shares his scenic adventure along the charming Avenue Vert, setting the stage for a captivating exploration into the world of open-source large language models (LLMs). Christian and Marc delve into the heart of AI, examining the strengths and limitations of open models like Mistral, Meta's Llama, and Google's Gemma compared to closed systems such as GPT, Gemini or Claude. Discover the power of open weights and how they provide users with the flexibility to harness pre-trained parameters for a myriad of applications, shedding light on the architecture and performance of these rapidly evolving models.
As we navigate the nuanced landscape of AI, we compare the practicalities and strategic advantages of open versus closed LLMs. While open-source models may require a steeper learning curve and higher initial investment in technical expertise, they offer unparalleled customisation for specialised tasks and niche programming languages. Conversely, closed models like GPT-4 and Gemini shine in delivering cost-effective solutions for more general applications. We also delve into the strategic deployment of LLMs for on-device applications and the critical importance of data sovereignty in fields like healthcare. Tune in to gain a comprehensive understanding of how these developments might impact your projects and reshape your industry.

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Music by Skilsel.
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1. #31 - Open vs. Closed LLMs: A Strategic Guide (00:00:00)

2. Exploring Open Source Language Models (00:00:05)

3. Comparing Open and Closed Language Models (00:15:00)