Companies are increasingly deploying smaller and midsize generative artificial intelligence models, favoring the scaled down, cost efficient technology over the large, flashy models that made waves in the AI boom’s early days. Unlike foundation models such as OpenAI’s GPT-4—which cost more than $100 million to develop and uses more than one trillion parameters, a measure of its size—smaller models are trained on less data and often designed for specific tasks. Nearly all model providers, including Microsoft and Google and startups like Mistral, Anthropic and Cohere, are moving to offer more of these types of models. Chief information officers say that for some of their most common AI use cases, which often involve narrow, repetitive tasks like classifying documents, smaller and midsize models simply make more sense. And because they use less computing power, smaller models can cost less to run. The shift comes as companies slowly move to deploy more AI use cases, while they are also under pressure to manage costs and returns on the pricey technology. “A giant LLM [large language model] that’s been trained on the entire World Wide Web can be massive overkill,” said Robert Blumofe, chief technology officer at cybersecurity, content delivery and cloud computing company Akamai. For enterprise use cases, he said, “You don’t need an AI model that knows the entire cast of ‘The Godfather,’ knows every movie that’s ever been made, knows every TV show that’s ever been made.”
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