Apple Trained its Apple Intelligence Models on Google TPUs, Not NVIDIA GPUs
Apple’s training data came from various sources, including the Applebot web crawler and licensed high-quality datasets. The company also incorporated carefully selected code, math, and public datasets to enhance the models’ capabilities. Benchmark results shared in the paper suggest that both AFM-server and AFM-on-device excel in areas such as Instruction Following, Tool Use, and Writing, positioning Apple as a strong contender in the AI race despite its relatively late entry. Tuttavia, Apple’s penetration tactic into the AI market is much more complex than any other AI competitor. Given Apple’s massive user base and millions of devices compatible with Apple Intelligence, the AFM has the potential to change user interaction with devices for good, especially for everyday tasks. Quindi, refining AI models for these tasks is critical before massive deployment. Another unexpected feature is transparency from Apple, a company typically known for its secrecy. The AI boom is changing some of Apple’s ways, and revealing these inner workings is always interesting.