distributed training

advancedwidespread

Distributed training means that an AI model is trained not by a single machine, but by many processors or computers working in coordination at the same time.

In plain terms

Imagine you need to translate a thousand-page book. A single translator might work on it for over a year, but a hundred translators could finish in a few weeks if they distribute the chapters well and regularly align terminology. Distributed training does the same with AI models: it splits the work among many machines.

It is needed because training today's large language models requires so much computing power that it would take hundreds or even thousands of years on a single graphics processing unit (GPU). Thousands of GPUs working together can do it in weeks or months.

The difficulty is not distribution, but coordination. Machines must continuously share what they have learned with each other; otherwise, each would steer the model in a different direction. Therefore, in such systems, the fast network between machines is as important as the processors themselves.

This distinguishes it from simple "more machines, more work" solutions: here, not many separate tasks run in parallel, but a single shared model is built by many hands.

How it works

Sharding: the training data is split into smaller batches, and each machine (or GPU) receives a different batch. If the model is too large to fit in a single processor's memory, the model itself is also partitioned.
Local computation: each machine works out, on its own batch of data, in which direction and by how much the model's settings (parameters) should be adjusted to get a better result.
Reconciliation: the machines combine their proposals over the network, typically by averaging them, so a single shared update results.
Update: the joint modification is applied by every machine to its own model instance, so before the next round, the same model is present everywhere again.
Iteration and saving: this loop repeats millions of times, and the system occasionally saves its state so that a machine failure does not require starting over from scratch.

An everyday example

When you read in the news that a company is building a data center with tens of thousands of GPUs for a new model, distributed training is typically taking place: these processors work together on a single model for weeks or months. The chatbot you are talking to is the result of such work.

Who is affected

developerWhen training or fine-tuning a larger model, you must decide how many machines to use and how to distribute the workload. → Code

entrepreneur, executiveThe cost and time of training a custom model are largely determined by how many machines you rent and how efficiently they work together.

bank customer, investorOne major reason for the huge demand for chipmakers, cloud providers, and data center builders is the computing power required for distributed training; the other is the mass use of ready-made models. → Money

everyonePartly for this reason, large AI models are so expensive and consume so much electricity, which ultimately shows up in prices and energy usage.

A common misconception

MythWith twice as many machines, the model is completed twice as fast.

In realityCoordination between machines consumes time and network capacity, so the speedup diminishes as the number of machines increases. In a poorly configured system, extra machines offer little help.

Limits and risks

Very expensive: due to hardware, electricity, and cooling costs, only a few organizations can afford to train the largest models.
Failures are common among many machines, so without regular backups, days or weeks of work can be lost.
Network speed between machines is a bottleneck: with slow connections, processors spend most of their time waiting for each other.
More machines do not replace good data: poor-quality training data will still produce a poor model, even when distributed, just faster.

Similar, but not the same

federated learning – There, the data remains with the owners (for example, on phones or in hospitals) due to data protection, while in distributed training the goal is speed, and the data is typically in a data center.
distributed inference – It distributes the use of an already trained model across many machines to serve users, not the training of the model.
multi-agent rendszer – There, multiple independent agents work together on a task, whereas in distributed training, machines work on creating a single model without an independent role.

Related concepts

Broader

model training, parallel computing, elosztott rendszer, AI infrastructure

Narrower

data parallelism, model parallelism, parameter server

On the edge

federated learning, GPU-klaszter, data center, supercomputer, computing capacity, checkpoint

A brief history

One milestone in distributed deep learning was Google's DistBelief system, introduced in 2012, which trained neural networks on clusters of thousands of machines and many thousands of processor cores. With the emergence of large language models, this method became a prerequisite for AI development.

Where things stand

At the end of 2026, every major large model is trained with distributed training on thousands or even hundreds of thousands of GPUs. The main challenge today is the efficiency of power supply, cooling and the network between machines.

Status: 2026 November

Also known as

distributed training · distributed learning · distributed training · distributed model training · distributed training · distributed machine learning · multi-GPU training · parallel training

Sources

This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.