Artificial intelligence companies seek big profits from ‘small’ language models

Artificial intelligence companies that have spent billions of dollars building so-called large language models to power generative AI products are now banking on a new way to drive revenues: small language models. Apple, Microsoft, Meta and Google have all recently released new AI models with fewer “parameters” — the number of variables used to train an AI system and shape its output — but still with powerful capabilities. The moves are an effort by technology groups to encourage the adoption of AI by businesses who have concerns about the costs and computing power needed to run large language models, the type of technology underpinning popular chatbots such as OpenAI’s ChatGPT. Generally, the higher the number of parameters, the better the AI software’s performance and the more complex and nuanced its tasks can be. OpenAI’s latest model GPT-4o, announced this week and Google’s Gemini 1.5 Pro, are estimated to have more than 1tn parameters. Meta is training a 400bn-parameter version of its open-source Llama model. As well as struggling to convince some enterprise customers to pay the large sums needed to run generative AI products, there are also concerns over data and copyright liability holding back adoption. That has led tech groups like Meta and Google to pitch small language models with just a few billion parameters as cheaper, energy-efficient, customisable alternatives that require less power to train and run, which can also ringfence sensitive data. “By having this much quality at a lower cost point, you actually enable so many more applications for customers to go in and do things that prohibitively there wasn’t enough return on that investment for them to justify really doing it,” said Eric Boyd, corporate vice-president of Microsoft’s Azure AI Platform, which sells AI models to businesses.

Full report : Microsoft, Meta, Google and others pitch smaller language models that are cheaper to build and train, to lower costs and hardware requirements for generative AI.