What is Distributed Training?
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.
Why It Matters for AI Builders
Directly governs the hardware efficiency and hardware-level token throughput when deploying foundation model pre-training, parameter scaling operations, and cluster orchestration; optimizing Distributed Training is a major factor in compute cost budgeting.
Detailed Deep Dive
Distributed training is a paradigm where the training workload of a large AI model is split across multiple processing units (GPUs or TPUs) and physical servers. As model sizes outgrow the memory of a single device, distributed training utilizes techniques like Data Parallelism (splitting the dataset batch), Model Parallelism (splitting layers across devices), and Pipeline Parallelism to coordinate training efficiently.
Frequently Asked Questions
Q: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.
Q:What are popular frameworks for distributed training?
PyTorch Distributed Data Parallel (DDP), Megatron-LM, and DeepSpeed.
Quick Facts
- CategoryHardware & Infrastructure
- Key ApplicationFoundation model pre-training, parameter scaling operations, and cluster orchestration.
Coverage Trend12 Weeks
Cite This Term
Reference this definition in your articles, research, or documentation to credit this source:
Distributed Training Media Coverage & Intelligence
PyTorch 2.13.0 Released with Torch.compile Performance Tuning and AOTInductor Enhancements
PyTorch 2.13 reached general availability, featuring improved Python 3.13 compilation speeds and enhanced multi-GPU distributed training APIs.
When Kubeflow meets Cilium: Debugging 60% idle GPUs in Kubernetes
The symptom that made no sense The first time we saw it, we didn't trust the dashboard. A distributed training job was scheduled and healthy - every pod was running, no crashes, no OOMKills, nothing in...
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.
Why your Kubernetes scheduler can't handle AI workloads
Imagine this scenario: You have a distributed training job with 16 worker pods, each requesting 1 GPU. 4 GPU are currently available. The default Kubernetes scheduler ( kube-scheduler ) may schedule those 4 pods while the remaining 12 stay pending. Meanwhile, those 4 GPU are reserved by pods...
PyTorch 2.13.0 Released with Torch.compile Performance Tuning and AOTInductor Enhancements
PyTorch 2.13 reached general availability, featuring improved Python 3.13 compilation speeds and enhanced multi-GPU distributed training APIs.