Distributed & Parallel Training

Training a modern AI model on a single computer is impossible - the model is too large to fit in one machine's memory, and the computation would take years. Distributed training splits the work across thousands of processors, usually specialised AI chips (GPUs or TPUs) connected by high-speed networks. There are several strategies: data parallelism splits the training data across machines, each processing a different batch simultaneously; model parallelism splits the model itself across machines when it's too large for any single device; pipeline parallelism chains processors together, each handling a different stage. Getting these strategies to work efficiently is one of the hardest engineering challenges in AI. The processors need to stay synchronised, network communication can become a bottleneck, and hardware failures are inevitable at this scale - a training run on 10,000 GPUs might expect several hardware failures per day. For business leaders, this matters because the engineering complexity and hardware requirements of distributed training create enormous barriers to entry. It's one of the main reasons only a handful of organisations can build frontier models, and it directly influences the competitive landscape of AI providers you might work with.