AI models are becoming smarter and more sophisticated and we’re increasingly using them to do everything, from making medical diagnoses easier to accurately predicting violent solar flares. And as teams work on building models that can so more, the volume and complexity of data require large AI models. Training AI models is not a one-size-fits-all process. Large models require billions, even trillions of training parameters and enormous datasets for accuracy. This primer on large scale model training provides insights into the training process and some of the challenges teams can expect.
What is large AI model training?
Large model training involves training large and complex AI models with very high volumes of data. There’s no clearly defined benchmark of what a large model is and with the rapid pace of AI evolution, models are growing ever larger and more complex with each iteration. Back in 2018, GPT-1 was considered a large-scale model with 117 million parameters. However, by 2023, GPT-4 boasted 1.7 million parameters.
Training models at this scale requires spreading the workload across different high-performance computing systems, a process known as distributed computing. This horizontal scaling increases the capacity of training hardware so it can handle massive datasets.
Teams also employ a technique known as parallelism which is designed to accelerate data processing by performing multiple tasks on the dataset at the same time.
- Data parallelism: Enables many different datasets to be processed simultaneously.
- Model parallelism: Spreads parts of the model across different machines (hardware).
- Pipeline parallelism: Distributes the distinct stages of a model across multiple processors.
Two-stage training process
While the overall AI model development lifecycle remains the same for large models, the training may use a two-step approach. The first step is pre-training and the second is fine-tuning.
- Pre-training: During this initial step, the model is exposed to broad datasets. Data may come from a range of sources including books, websites and existing databases. The goal is to help the model learn broad patterns and give it a generalized understanding of language including linguistic structures, syntax, and semantics. Pre-training is useful in large language models.
- Fine-tuning: This refining stage focuses on smaller, task-specific datasets that help the model gain task or domain level expertise. Fine-tuning builds on the existing pre-training to reduce the amount of task-specific data the model needs to produce an accurate output.
Challenges of large model training
Building and training large-scale AI models involves plenty of innovation, but it also brings challenges.
- Computational resources: Project teams may require extensive infrastructure and hardware to produce high quality models. GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) can be expensive.
- Energy needs: Large model training is an intense and demanding process that consumes tremendous energy which may pose sustainability concerns.
- Data management: Large models require vast amounts of data. Teams may struggle to find the high volumes of good data they need. Storing and pre-processing this data can also be tedious.
Large-scale AI model training can help create more complex and efficient models, that get increasingly better at solving problems. Better research and experimentation may give us superior training methods in the future.
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