Palantir says a fine-tuned 4B Nvidia model beat bigger AI rivals at lower cost

McCarthy Building Companies trained Nvidia's Nemotron 3 Nano 4B on its own construction data and reportedly topped larger models on risk classification In AI, the default assumption has been simple: bigger models win. Palantir just presented evidence that a much smaller one can beat them at a specific job, and do it for less money. The company reported that a fine-tuned, 4-billion-parameter Nvidia model outperformed larger models on a task built by McCarthy Building Companies, at a lower cost. A small model with a very specific education The model in question is Nvidia’s Nemotron 3 Nano 4B. McCarthy, a construction firm, fine-tuned it on more than 100,000 labeled requests-for-information, or RFIs. For the non-builders in the room, an RFI is the formal question a contractor sends when plans, specs, or site conditions don’t line up. They are the paper trail of everything that could go sideways on a job site. That makes them a goldmine for spotting trouble early. McCarthy used the trained model to classify potential project risks and predict how those risks might hit schedules and budgets. According to the findings, the tuned model delivered superior performance on that classification...