Definition
Distributed training splits the job of training an AI model across many machines running at once, so a huge job finishes far faster than on one computer.
At a glance
- One machine can take weeks or months to train a large model; many machines cut that to days[4].
- Data parallelism: each machine gets a full copy of the model but a different slice of the data.
- Model parallelism: when a model is too big for one chip, the model itself is split across machines[3].
- The tradeoff is cost and complexity: big GPU clusters are expensive and harder to coordinate.
Why it matters
The largest models hold more data than one machine can fit in memory[2]. Spreading the work across machines running in parallel turns a months-long job into a days-long one[1], meaning faster experiments, quicker time to market, and models that would otherwise be impossible.
When to use
Distributed training runs on clusters of GPU chips that are costly to rent and must be coordinated to avoid idle machines[5]. The largest models use tens of thousands of GPUs. But if your training is slow or your data is growing, even a handful of machines can speed up results and is usually worth the setup.
Bottom line
It trades extra cost and setup for speed and scale, and it is what makes today’s largest AI models possible at all.
References
- What is distributed training? - Azure Machine Learning. Microsoft learn.microsoft.com
- What Is Distributed Machine Learning? IBM www.ibm.com
- Distributed Parallel Training: Data Parallelism and Model Parallelism — Luhui Hu. Towards Data Science towardsdatascience.com
- Inside multi-node training: How to scale model training across GPU clusters. Together AI www.together.ai
- What is the cost of training large language models? CUDO Compute www.cudocompute.com
Comments
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