IBM Corp. is leaning into compact, specialized models — such as its new Tiny Time Mixers — to tackle network automation challenges where traditional large language models fall short. The key lies in understanding time-series data, something most large language models simply weren’t built to handle, according to Andrew Coward, general manager of software networking at IBM. “There’s new models, and IBM’s built one called Tiny Time Mixer. Very small parameters, million parameters, and they understand time. We can take network data, and then we can apply it to weather information or TV schedules. Then we can make predictions about what’s likely to happen. What we are seeing is the democratization of AI,” he said. “It’s almost free to put data in and run it against AI models, but if you need to train it, that’s the expensive bit. The training piece is coming down massively in costs.” Using small models, IBM helps address telco infrastructure problems, such as bandwidth congestion and poor network coverage. This explains why AI model accuracy takes center stage, Coward pointed out.