What makes GPUs so crucial for AI workloads? Is it just about raw processing power, or is there more to it? As we explore the world of AI infrastructure, understanding the role of GPUs is essential.
Let's dive into the math behind AI. At its core, AI is all about mathematics, and matrix multiplication is a critical component. Whether you're training a model to recognize images or predict outcomes, the data is converted into massive arrays or matrices of numbers. Training the model involves continuously multiplying these vast matrices.
CPUs vs. GPUs: A core difference
So, what's the difference between CPUs and GPUs? While CPUs have a few powerful cores that execute tasks sequentially, GPUs are built with thousands of smaller cores designed for parallel processing. This architecture is perfect for handling the complex math required for AI workloads.
By grasping the fundamental differences between CPUs and GPUs, you can unlock the true potential of your AI infrastructure. For large, production-ready models, scaling multiple GPUs across a cluster is essential. This capability allows developers and platforms to tailor the size of their inference platform, giving them vast flexibility in managing the largest modern AI models.
Scaling beyond a single chip
As we've discussed, the parallel processing power of GPUs is a game-changer for AI workloads. However, for truly massive models like LLaMA, even a single, powerful GPU might not be enough. This is where the scalability of GPU architecture comes into play.
During Civo Navigate London, we held an AI panel that looked into the rapidly evolving landscape of artificial intelligence. During this session, one of the speakers touched upon the importance of scalability in AI workloads, where being able to process vast amounts of data quickly is crucial.
"At the end of the day, everyone in Agentic AI is solving something called the context problem out of a massive ocean of information in an enterprise. How do you find the exact right piece of information to do that task?" - Abdul Hummaida, Founder & CTO of Covecta
One of the key advantages of GPUs is their ability to scale both vertically and horizontally. Vertical scaling involves having multiple GPUs within a single server node, allowing you to pack more processing power into a single machine. Horizontal scaling, on the other hand, involves creating a cluster of these GPU-enabled nodes to handle the workload. This clustering enables developers and platforms to tailor the size of their inference platform, giving them vast flexibility in managing the largest modern AI models.
For instance, when working with a model like LLaMA, a single top-tier GPU (like an Nvidia H100) might not be sufficient. The model may require a cluster of eight such GPUs just to run at a partial context length. This level of scalability is what truly accelerates AI workloads, allowing platforms to piece together computing power to meet their specific needs.
Training vs. inferencing: Two sides of the GPU coin
Now, you might be wondering what's next after training a model. The conversation often focuses on training, but the GPU's work doesn't stop once the model is complete. The second, and often more active, phase is inferencing. So, what is inferencing?
In simple terms, inferencing is the process of using a trained model to make predictions or generate output based on new data. It's what happens when you "call" the model and ask it for output. For example, if you've trained a neural network to distinguish between pictures of dogs and cats, inferencing is when you pass a new picture through it, and it correctly categorizes it as a dog.
In the world of large language models (LLMs), inferencing is what's happening when you use platforms like relaxAI. The model's core task is simply predicting the next word in a sentence. Because the model has been trained to infer in the best possible way, it gives you a continuous, coherent stream of words. Even though inferencing isn't training, it still requires the model to quickly process input through its vast matrix of parameters to arrive at the best prediction. Therefore, it relies just as much on the parallel processing power of the GPU to deliver a fast, seamless user experience.
By understanding the importance of scalability and the role of GPUs in both training and inferencing, you can better appreciate the complexity and power of modern AI workloads.
Explore more about AI inferencing in this blog Mostafa Ibrahim >
Summary
As you build your AI infrastructure, it's essential to understand the critical role that GPUs play in accelerating your workloads. At its core, AI is all about math - specifically, matrix multiplication. And because matrix multiplication is inherently parallel, GPUs are the perfect fit. With their massive parallel cores, GPUs can handle the complex math required for both training and inferencing, making them a crucial component of any AI infrastructure.
As Josh Mesout, Chief Innovation Officer at Civo, highlighted the importance of having a robust and vertically integrated AI infrastructure to support the development of AI models:
"I think it's really about making sure that we can get vertically integrated AI in the UK and that's not just about making sure that... we own the entire supply chain but that we've got the underlying expertise in the supply chain to be able to do so."
When it comes to large, production-ready models, scaling multiple GPUs across a cluster is not just a nice-to-have, it's a must-have. By doing so, you can unlock the full potential of your AI initiatives and deliver fast, seamless experiences to your users. So, as you plan your AI infrastructure, keep in mind the importance of GPUs and the need to scale them to meet your needs.
Watch the full recording from Civo Navigate London to learn more about GPUs and AI workloads 👇
