Definition
Distributed Training
Distributed Training is the practice of partitioning machine learning workloads (data or parameters) across multiple compute processors (GPUs/TPUs) to accelerate training times for large neural networks.
Frequently Asked Questions
What is Data Parallelism vs. Model Parallelism?▼
Data parallelism splits the dataset across devices, running copies of the model. Model parallelism splits the model layers across different GPUs because the model is too large to fit in a single device's VRAM.
What are popular frameworks for distributed training?▼
PyTorch Distributed Data Parallel (DDP), Megatron-LM, and DeepSpeed.
Distributed Training Media Coverage & Intelligence
CoreWeaveJun 18, 2026
What a Reference Architecture for Distributed AI Training Actually Looks Like
Scaling AI training changes how systems fail. Learn the four architectural layers required for reliable distributed training at production scale.