Author: Mohammad Mahdi Derakhshani

Date: October 7, 2025

Keywords: Distributed Training, Tensor Parallelism, Data Parallelism, Pipeline Parallelism, 3D Parallelism

Social Media: X: @mmderakhshani LinkedIn: @mmderakhshani GitHub: @mmderakhshani Email: [email protected], [email protected] Website: mmderakhshani.github.io Extra Resources: **Picotron & Picotron Tutorial**

Distributed Training in Transformers

Introduction

This guide examines key parallelization techniques for training large language models (LLMs) efficiently. With models scaling to billions and trillions of parameters, distributed training across multiple GPUs and nodes has become essential, not optional. Modern LLMs require coordinated computation across hundreds or thousands of processing units, enabled by five critical approaches to distributed training: from process management foundations to advanced parallelization architectures. These methods allow researchers and engineers to overcome computational challenges while maintaining efficiency and scalability.

Why Distributed Training Matters

Modern language models present unique challenges that make distributed training not just beneficial, but essential:

• Memory Constraints: Models with billions of parameters often exceed the memory capacity of even the most powerful individual GPUs
• Computational Intensity: The computational requirements for training large models can extend training times to impractical durations without parallelization
• Resource Optimization: Distributed training enables better utilization of available hardware resources, reducing both training time and energy consumption
• Scalability: As models continue to grow, distributed approaches provide a pathway for handling even larger architectures

Overview of Parallelization Strategies

Each parallelization technique addresses different aspects of the distributed training challenge:

Process Group Management forms the foundation, providing the communication infrastructure that enables coordination between distributed processes. Without robust process group management, other parallelization strategies cannot function effectively.

Tensor Parallelism tackles the challenge of individual operations that are too large for a single device, splitting tensors and computations across multiple GPUs within the same operation.

Data Parallelism addresses training throughput by distributing different batches of data across multiple devices, allowing simultaneous processing of larger effective batch sizes.

Pipeline Parallelism optimizes memory usage and computational efficiency by distributing different layers or stages of the model across different devices, enabling overlapped computation.

Understanding these complementary approaches—and knowing when and how to combine them—is crucial for anyone working with large-scale transformer models in research or production environments.