Bigger Isn't Always Better: Why Enterprise AI is Turning to Smaller, Sovereign Models | Utilizing AI Episode 39
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Bigger Isn't Always Better: Why Enterprise AI is Turning to Smaller, Sovereign Models | Utilizing AI Episode 39
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Not every task demands a multi-trillion parameter model with massive a context window, and we are seeing increasing success with small models designed with efficiency in mind.
This episode of Utilizing AI features Nick Patience and Brad Shimmin of The Futurum Group discussing the push and pull between frontier models and small domain-specific models.
Quantization, novel open weights models, and even local models give us all sovereignty over our data, at least to some extent. They also give us financial control, an increasingly important concern as AI service providers are charging increasingly large amounts for model use.
Patience and Shimmin are uniquely positioned in the industry to hear from all constituencies in the AI world, and provide their observations on the desire to take control of their software and model stack. All of these firms are starting to re-focus on efficiency, performance, and return on investment to optimize their AI applications even as the state of the art keeps pushing forward.
This and more on Utilizing AI, part of The Futurum Group Podcast Network.