# OpenAI reportedly buys tens of thousands of Mac minis for AI training > Source: > Published: 2026-08-31 04:06:26+00:00 Photo: Merlin Lightpainting / Pexels # OpenAI reportedly buys tens of thousands of Mac minis for AI training The AI lab is stacking Apple silicon to train reinforcement learning agents, a meaningful departure from its traditional reliance on GPU cloud clusters. OpenAI has quietly assembled one of the more unusual hardware fleets in modern AI history: tens of thousands of Apple Mac minis and Mac Studios, purpose-built for reinforcement learning workloads and training computer-use agents. The report, published by *The Information*, suggests the lab is leaning into Apple silicon in a way that would have seemed far-fetched even two years ago. Neither OpenAI nor Apple has publicly confirmed the arrangement, but the scale described is substantial enough to be market-moving on its own. ## Why Mac minis, of all things The answer has less to do with Apple brand loyalty and more to do with memory architecture. Apple’s unified memory design pools RAM across the CPU and GPU on a single chip, which turns out to be genuinely useful for workloads that involve multi-step computer operations: navigating software interfaces, drafting documents, organizing email, the kind of tasks that computer-use AI agents are trained to perform. Traditional GPU clusters are purpose-built for raw matrix math, the kind that powers large-scale model pre-training. Reinforcement learning with computer-use agents is different. It requires the AI to run inside an operating system, observe what’s on screen, take actions, and receive feedback, over and over, millions of times. That workflow is memory-bound and parallelism-light compared to transformer pre-training, which makes Apple’s architecture a surprisingly practical fit. OpenAI is not alone in figuring this out. Anthropic, the AI safety company backed by Google and Amazon, has adopted a similar approach by renting Mac mini capacity through Amazon Web Services to handle its own reinforcement learning tasks. ## Apple’s supply chain is already feeling it The demand spike has been visible in Apple’s order books. Delivery times for customized, high-RAM Mac mini and Mac Studio configurations have stretched to weeks or months in some cases. Apple responded on August 25, 2026, refreshing both product lines ahead of schedule. The new Mac mini lineup introduced M6 and M5 Pro chip options, while the Mac Studio moved to M5 Max and M5 Ultra configurations. The timing, five days before *The Information*‘s report, is probably not a coincidence. Apple’s Mac segment has been the fastest-growing slice of its hardware business in recent quarters. Revenue hit $10.4 billion in the most recent quarter, a 29% increase compared to the same period a year earlier. ## Nvidia is paying attention Nvidia, which effectively owns the AI training hardware market through its GPU dominance, has reportedly begun viewing Apple as a meaningful competitor in local AI processing. Mac minis, which start at a fraction of the cost of a single enterprise GPU server, offer a cost-effective path to horizontal scaling for the right workloads. Buying ten thousand of them is still an enormous capital expenditure, but the economics look different when the alternative is reserving equivalent cloud GPU time for tasks those chips aren’t optimized for. **Disclosure:** This article was edited by Editorial Team. For more information on how we create and review content, see our [Editorial Policy](https://cryptobriefing.com/editorial-policy/).