Everything you need to know about Large AI Model Training

When looking back at the role artificial intelligence (AI) has played in revolutionizing different industries that would typically require human intelligence, it is important to consider the next steps in this journey and how it is starting to evolve. With the growth of the industry, the volume and complexity of data are becoming unmanageable for pre-existing AI models.

This has resulted in the need for large AI model training, which requires substantial computational power, particularly from Graphics Processing Units (GPUs) that are specifically designed to handle the high level of parallel processing involved in training these models. In this blog, we will explore the importance of large AI model training for the growing industry, plus explain how it works, and outline the challenges and future predictions.

The history of artificial intelligence

Dating back to the 1990s, Margaret A. Boden described artificial intelligence (AI) as “the study of how to build or program computers to enable them to do what minds can do.” When looking at more recent studies, the depth of the concept increases with the terminology of ‘third-generation artificial intelligence’ being used. According to a study from Science China, this is a combination of the “knowledge-driven methods of the first generation and the data-driven methods of the second generation, using the four elements of knowledge: data, algorithms, and computing power.”

Some believe that AI stems from pre-20th century in ancient Greek mythology, but the term was not coined until the 1950s whereby the study of AI began. Over the past 70 years, we have seen a range of milestones in the AI field, from Deep Blue in the 1990s all the way to Siri in 2011, and GPT-1 in 2018.

Joey de Villa spoke about the topic of AI during a Navigate North America 2024 session, where he looked into the history of AI, current trends, and the ethical considerations we must navigate as this technology evolves. Watch the full session here:

What is large AI model training?

The process of training AI models based on a high amount of data can be defined as large-scale AI model training. With the amount of data progressively growing, we are starting to see more large-scale AI models that are made up of complex architectures and high-powered computational resources.

Using potentially trillions of data pieces, there is no true benchmark to define what can be classified as a large-scale model. A prime example of this is GPT-1 which was considered a ‘large-scale’ model with 117 million parameters and 600 billion tokens. Then, in 2023, the latest model GPT-4 was released which had roughly 1.7 trillion parameters and 13 trillion tokens. This makes it apparent how the vast size of the dataset required for models to be classified as a large-scale model is growing exponentially.

Epoch AI conducted a study with 81 large-scale models across 18 countries to demonstrate the timeline of growth with models such as AlphaGo to Gemini:

How does large AI model training work?

Now that we have an understanding of what large AI model training is, let’s take a look at how it all works. In the image below, we have outlined the essential steps required for AI model training to work:

Training large-scale AI models involves a series of structured steps that differ from the standard AI training process due to the massive amount of data and computational resources required. These resources include powerful GPUs, which are essential for efficiently processing and training models at such a large scale. Below is an overview of the key aspects involved in large-scale AI model training:

The challenges of large AI model training

With the sheer amount of data that is involved in large AI model training, it is more important than ever to acknowledge and address the challenges faced by those utilizing AI models. These challenges include a range of issues, including data management, the complexity of managing vast datasets, and the energy consumption required. Being able to navigate these challenges successfully is essential for advancing AI in a responsible and sustainable manner.

Alongside the team at Pieces, we hosted an online meetup that looked into the complexities associated with Large Language Models (LLMs) and how to manage them. This meetup focused primarily on deploying and managing large AI models, particularly on Kubernetes, while covering emerging trends such as dynamic resource allocation and environmental considerations. Watch the full recording here.

The future of large AI model training

While the evolution of AI is ongoing, people are beginning to speculate about what we can expect from the future of large AI model training. The core belief is that we will begin to move towards more automation, increased collaboration between industry and academia, greater reliance on open-source models, and a balanced approach that leverages both AI capabilities and human expertise.

One of the major points we can consider surrounding the future of large AI model training is the evolution of augmenting developer tasks with tools such as GitHub Copilot to make more automated processes where tools are able to understand context, pull feature branches, and submit code for review without any human intervention. Some of the other predictions that can be made for the future include:

• The collaboration between industry and academia
• Human-AI collaboration to ensure accuracy and innovation
• The potential for novel applications which could involve combining different models for various purposes

During Navigate North America 2024 in Tampa, Florida, we spoke with industry experts Josh Mesout, James Gress, Brandon Dey, and Cate Gutowski, about their predictions surrounding the future of AI and machine learning. Watch the full recording here.

Over the years, we have hosted other panel discussions that touch upon similar topics - for more on these, check out the links below:

• Europe 2023: What does the future of AI and ML look like in 2024
• North America 2023: The future of machine learning and AI

What are we doing at Civo?

At Civo, we’re transforming how businesses approach machine learning, scientific computing, and generative AI with our cloud GPU-powered compute and Kubernetes offerings. By leveraging industry-leading NVIDIA GPUs, we provide high-performance computing solutions that are scalable, cost-effective, and easy to integrate into your existing infrastructure.

Whether you're working on AI training, high-performance computing, or graphics-intensive tasks, Civo's GPU solutions offer the power, flexibility, and sustainability you need to succeed.

Find out more today.

Summary

The development of artificial intelligence necessitates the training of increasingly large and complex models to handle vast data sets, which in turn requires powerful GPUs to manage the intensive computational demands. From the early days of Deep Blue to modern giants like GPT-4, AI's evolution reflects its growing capability and complexity. With the future of the industry unclear, it promises greater automation, enhanced collaboration between academia and industry, and innovative applications, all while balancing AI's capabilities with human expertise.

If you want to learn more about what we are doing at Civo, click here.