LLM Multi-GPU Training: A Guide for AI Engineers

Author(s): Burak Degirmencioglu

Originally published on Towards AI.

To keep up with the rapid evolution of large language models (LLMs), multi-GPU training has become a crucial necessity for AI engineers. As models scale from billions to trillions of parameters, a single GPU is no longer sufficient for both training from scratch and fine-tuning existing models. As models grow from billions to trillions of parameters, the limitations of a single GPU become glaringly obvious. The need for massive resources — both in terms of compute and memory — is the primary bottleneck for many researchers and developers.

This is where the strategic use of multi-GPU and multi-node setups becomes not just an option, but a necessity, allowing us to overcome these limitations and push the boundaries of what is possible in AI.

What are the challenges when training Large Language Models(LLMs)?

The biggest hurdles in training LLMs are the enormous computational resources and memory required. A model like GPT-3, with 175 billion parameters, simply can’t fit on a single consumer-grade GPU. This is not just about model weights, but also activations, gradients, and optimizer states, which all consume precious VRAM. Without distributed training, you’re faced with an impossible memory wall. A common example of this would be trying to run a model that requires over 80GB of VRAM on a single GPU with only 24GB. The process would fail immediately with an out-of-memory error.

How can distributed training strategies help?

To overcome the limitations of a single GPU, engineers use distributed training, which involves distributing the model and data across multiple GPUs or even multiple machines.

This approach addresses three key challenges: memory usage, compute efficiency, and communication overhead . Distributed training allows us to scale beyond the limitations of a single piece of hardware, enabling us to handle models that would otherwise be impossible to train. For instance, using multiple GPUs allows for the collective memory of the GPUs to be used, effectively creating a larger virtual memory pool for the training process.

Key Parallelism Techniques for LLM Multi-GPU Training

The most common methods for distributing a training job across multiple GPUs are through various forms of parallelism. Understanding these is crucial for efficient and scalable training.

1) Data Parallelism

2) Distributed Data Parallelism (DDP)

3) Model Parallelism(Tensor Parallelism/Pipeline Parallelism)

4) Expert Parallelism

5) Context Parallelism

6) Sequence Parallelism (SP)

Data Parallelism (DP) is perhaps the most straightforward approach. It involves replicating the entire model on each GPU and then distributing different chunks of the training data to each one. Each GPU trains on its unique subset of data and calculates gradients. These gradients are then averaged and synchronized across all GPUs to update the model weights.

A popular implementation of this is Distributed Data Parallelism (DDP) in PyTorch, which is a highly efficient way to do data parallelism. It uses torch.distributed and init_process_group to manage the communication between GPUs. A good example of this is a scenario where you have a dataset of 100,000 images, and you distribute it across 4 GPUs, with each GPU processing 25,000 images at a time. The DistributedSampler is a helpful tool that ensures each GPU gets a unique, non-overlapping subset of the data.

Moving beyond data parallelism, Model Parallelism becomes essential when the model itself is too large to fit into the memory of a single GPU.

Tensor Parallelism involves splitting individual layers or the model’s tensors across different GPUs. This way, each GPU only holds a part of the model’s weights. For example, a large matrix multiplication operation can be partitioned, with each GPU computing a piece of the result. This technique is particularly useful for giant embedding tables or transformer blocks that exceed a single GPU’s capacity

Pipeline Parallelism focuses on distributing different layers of the model to different GPUs. Imagine a model with 12 layers. You could assign layers 1–4 to GPU 1, layers 5–8 to GPU 2, and layers 9–12 to GPU 3. Data is passed from one GPU to the next in a sequential “pipeline.” This can be visualized as an assembly line where each worker (GPU) performs a specific task (a set of layers) and passes the result to the next worker.

Expert Parallelism, as seen in models like Mixture of Experts (MoE), distributes different “expert” neural networks across various GPUs. When a token needs to be processed, a gating network determines which expert(s) should handle it, routing the data to the appropriate GPU. This is an effective way to scale models with sparse activation patterns.

In addition to the main types, other techniques exist. Context Parallelism is about distributing the input context or sequence across different devices, while Sequence Parallelism (SP) is a more advanced technique for handling very long input sequences. It works by parallelizing the computation within a single transformer layer, which can be combined with DDP to further optimize training for long sequences.

For example, instead of running the entire sequence on one GPU, you could split a 1000-token sequence into two 500-token chunks, with each GPU processing one chunk.

Optimization Techniques for More Efficient Training?

Beyond parallelism, there are numerous techniques to make multi-GPU training more efficient and to reduce memory consumption.

1) Zero Redundancy Optimizer (ZeRO)

2) Activation Recomputation

3) Gradient Accumulation

4) Fused Kernels

5) Mixed Precision Training

The Zero Redundancy Optimizer (ZeRO), from the DeepSpeed library, is an optimizer that shards the model states (optimizer states, gradients, and parameters) across GPUs, dramatically reducing memory overhead.

Activation Recomputation, or gradient checkpointing, saves memory by not storing all intermediate activations during the forward pass, instead re-calculating them during the backward pass.

Gradient Accumulation allows you to use a larger effective batch size than your GPU memory allows by computing gradients over several mini-batches and summing them before a single weight update (Source: Hugging Face Nanotron).

Fused Kernels such as FlashAttention, which is specifically designed to optimize the self-attention mechanism reduce memory access and increase speed.

Mixed Precision Training uses both 16-bit and 32-bit floating-point numbers to reduce memory usage and increase throughput. Lower-level GPU architectural improvements and custom kernels also play a significant role in improving training efficiency.

Why are benchmarking and best practices so important?

While all these techniques offer powerful solutions, implementing them correctly requires a methodical approach.

The importance of benchmarking cannot be overstated. You need to measure your training throughput (the number of tokens or samples processed per second) and GPU utilization to understand what’s working and what isn’t. For instance, if your GPU utilization is low, it might be an indication of a communication bottleneck, a key challenge in distributed training. Benchmarking helps you identify these issues and fine-tune your configuration for maximum efficiency. Learning from common pitfalls, such as network failures or synchronization issues, is also crucial for building robust and scalable training pipelines.

Conclusion

Training large language models is a complex but rewarding task, and leveraging multi-GPU and distributed training strategies is essential to success. We’ve explored the fundamental challenges, the core concepts of data and model parallelism, and the crucial role of optimizations like ZeRO, and FlashAttention. Mastering these techniques is what enables us to build and fine-tune the next generation of AI models.

The landscape is constantly evolving with new architectures and tools, so staying informed and applying these principles will be key to unlocking even more powerful models.

Mastering these multi-GPU training techniques is the key to building and fine-tuning the next generation of AI models. What challenges have you faced in your own projects?

Share your experiences below to continue the conversation.

References:

A Comprehensive Guide to Multi-GPU LoRA Fine-Tuning with Distributed Data Parallelism and Sequence…

1. Introduction

dhnanjay.medium.com

Scaling Deep Learning with PyTorch: Multi-Node and Multi-GPU Training Explained (with Code)

Train GPT-2 model on scale using PyTorch’s Distributed Data Parallel (DDP)

medium.com

The Ultra-Scale Playbook – a Hugging Face Space by nanotron

This application displays detailed training data for large language models (LLMs) on GPU clusters, showing performance…

huggingface.co

Parallelism methods

We're on a journey to advance and democratize artificial intelligence through open source and open science.

huggingface.co

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Published via Towards AI