A 2-billion-parameter model that trades blows with one ten times its size is not an accident of architecture. It is the product of a teacher pouring its full probability distribution into a student, token by token.
A 75% idle GPU is usually not a compute problem. Across the configurations one VLDB study measured, DNN training spent between 10% and 70% of every epoch blocked on I/O, and the storage system was not busy. The data path has its …
For years the field raced to build bigger models. Then a 2022 study showed most of those giants were undertrained, and quietly redrew the map of how to spend a compute budget.
DeepSeek shipped a 1.6T-parameter model in April 2026 with native Ascend inference support while OpenAI committed $500B to Stargate. Both can be right, but only one of them is a moat.
Change one bit in one weight of a ResNet and, three epochs later, test accuracy differs by more than ten points. Training is a chaotic process, so a seed is not a control variable but a draw from a distribution. Most published an…