Apple has revealed that it didn’t use Nvidia’s hardware accelerators to develop its recently revealed Apple Intelligence features. According to an official Apple research paper (PDF), it instead relied on Google TPUs to crunch the training data behind the Apple Intelligence Foundation Language Models. Systems packing Google TPUv4 and TPUv5 chips were instrumental to the creation of the Apple Foundation Models (AFMs). These models, AFM-server and AFM-on-device models, were designed to power online and offline Apple Intelligence features which were heralded back at WWDC 2024 in June. AFM-server is Apple’s biggest LLM, and thus it remains online only. According to the recently released research paper, Apple’s AFM-server was trained on 8,192 TPUv4 chips “provisioned as 8 × 1,024 chip slices, where slices are connected together by the data-center network (DCN).” Pre-training was a triple-stage process, starting with 6.3T tokens, continuing with 1T tokens, and then context-lengthening using 100B tokens. Apple said the data used to train its AFMs included info gathered from the Applebot web crawler (heeding robots.txt) plus various licensed “high-quality” datasets. It also leveraged carefully chosen code, math, and public datasets. Of course, the ARM-on-device model is significantly pruned, but Apple reckons its knowledge distillation techniques have optimized this smaller model’s performance and efficiency. The paper reveals that AFM-on-device is a 3B parameter model, distilled from the 6.4B server model, which was trained on the full 6.3T tokens.
Full story : Apple shuns NVIDIA GPUs, says its AI models were trained on Google’s custom chips.
