Explore distributed inference and training with MLX [video] (developer.apple.com)

Apple's MLX team has introduced a groundbreaking approach to distributed inference and training using multiple Macs, allowing users to efficiently scale machine learning workloads without relying on costly cloud infrastructure. In a detailed presentation from WWDC 26, they demonstrated how to overcome limitations of single-device processing by utilizing multiple Macs to handle larger models and complex AI tasks. By employing Remote Direct Memory Access (RDMA) over Thunderbolt 5 for low-latency and high-bandwidth communication, MLX facilitates swift data transfer between machines, enhancing performance for distributed workloads. The significance of this development lies in its potential to democratize access to powerful AI capabilities, enabling individuals and smaller organizations to undertake demanding computational tasks traditionally reserved for high-end data centers. With frameworks like JACCL enhancing collective communication and protocols for easy setup, MLX allows users to seamlessly manage distributed tasks—from simple model inference to robust training pipelines. This innovation not only accelerates processing speeds—demonstrated by nearly three times the speed of inference across clustered Macs—but also addresses scalability challenges for managing large AI models exceeding the memory capacity of a single machine.