Distributed Training

Distributed training is the practice of splitting the computational work of training a machine learning model across multiple processors, GPUs, or machines simultaneously. It encompasses strategies such as data parallelism (each device trains on a different shard of data with identical model weights), model parallelism (the model itself is partitioned across devices), and pipeline parallelism (layers are staged across devices). These techniques have become essential for training models too large or too data-intensive to fit on a single accelerator.

Modern frontier models — LLMs, diffusion models, multimodal systems — require hundreds to thousands of GPUs working in concert; no single accelerator can accommodate their memory or compute demands. AI infrastructure roles at companies like Google, Meta, NVIDIA, Mistral, and Hugging Face explicitly require distributed training expertise because training efficiency directly determines how fast teams can iterate and how much they spend per training run. Engineers who can tune parallelism strategies, reduce communication overhead, and maintain fault tolerance across multi-node clusters are among the most sought-after profiles in applied AI in 2026.