What Is a GPU for AI?

GPUs for AI are powerful processing units designed to boost system performance when processing a large volume of data simultaneously.

Their architecture is optimized for a form of computation known as parallel processing, which makes them effective at demanding applications, including AI and machine learning, scientific simulations, and rendering graphics for gaming.

Composed of hundreds or even tens of thousands of cores—or processing units—GPUs have a unique parallel structure that makes them fast and efficient at doing many calculations simultaneously. For that reason, GPUs are considered a vital piece of hardware for many advanced AI use cases.

AI algorithms perform a large number of matrix multiplications and vector operations to function. These operations can easily exceed the performance capabilities of a computer system, especially when the number of calculations is particularly vast.

Often, GPUs are enlisted to provide the additional resources needed to accelerate these operations. For example, having a GPU in your hardware configuration can help reduce the time it takes to train a large-scale neural network, which could take days or weeks to complete on the central processing unit (CPU) alone. In short, the GPU could be said to supercharge AI operations.

Role of GPUs for AI

Because GPUs can deliver accelerated computational performance, they are often superior when working with large and complex AI models, including many types of deep learning models. On the other hand, they may be excessive for AI applications that use smaller models and require fewer resources. It’s important to choose hardware that provides the right amount of performance based on the scale and complexity of the workload at hand.

Large AI Workloads

What exactly is a large and complex model? A model is said to be large when it has been trained on a large dataset and, as a result, contains a large number of parameters—that is, the internal variables used to make predictions. Complexity refers to the depth, width, or intricacy of a model’s architecture and to the model’s ability to handle complex data, such as data with a large number of variables or data that contains errors.

For example, large and complex deep learning models are used for applications like deep reinforcement learning (teaching robots to walk, autonomous cars), advanced computer vision applications (monitoring deforestation with satellite imagery), doing sophisticated generative AI (GenAI) (high-resolution images, training large language models (LLMs) on Wikipedia), along with countless other AI applications that incorporate a very large amount of data. These applications often necessitate GPU-accelerated computing.

GPUs are effective at driving compute-intensive models across multiple phases of deployment. They can substantially speed up the processes of:

• Training—feeding an AI model data
• Fine-tuning—enhancing an AI model for improved accuracy at specific tasks
• Inference—using a trained AI model to draw conclusions on new data when substantial compute resources are demanded

Smaller AI Workloads

While GPUs are ideal for boosting computationally heavy AI workloads, other types of hardware can be more effective for small-to-midsized workloads.

In reality, smaller models are frequently deployed for many industry-specific use cases. For instance, chatbots and virtual assistants can run on leaner models trained or tuned to smaller domain-specific datasets. The same goes for applications like speech to text, speech recognition, sentiment analysis, time series forecasting, and anomaly detection.

These industry-optimized models use smaller datasets and, therefore, require fewer compute resources. That means the CPU alone can power them in many instances. Moreover, some CPUs have integrated AI accelerator engines and neural processing units (NPUs) already built in, further expanding their AI capabilities.

The result is that CPU resources can be used instead of GPUs when a large model is not needed, allowing technical decision-makers to implement a more cost-effective hardware plan.

Benefits of AI GPUs

Capable of performing trillions of calculations per second, GPUs can be indispensable for accelerating large and complex AI models. Their benefits include:

• Parallel processing: The parallel architecture of GPUs is optimized for high throughput—or the rate at which data can be processed. This makes GPUs highly efficient at executing the vast number of operations involved in training neural networks and using them for inference. That efficiency translates to faster processing times, significantly accelerating AI models.
• Scalability: Multiple GPUs can run in parallel with the workload divided between them. Grouping GPUs into clusters can further expand an AI system’s computational capabilities. The technique is often implemented in data centers and research labs when training complex neural networks. Very large clusters of server-class GPUs can be used to build supercomputers and enable high performance computing.
• Optimized software: GPU acceleration is typically used within an AI framework, like TensorFlow or PyTorch. These collections of libraries and tools are optimized for parallel processing, allowing developers to tap into GPU resources more easily.

Considerations

While GPUs excel at executing heavy AI workloads, cost and energy usage concerns should be considered when choosing the optimal hardware for AI applications:

• Cost-effectiveness: GPUs are cost-effective solutions for training and inference use cases with compute-intensive workloads, such as when deploying complex neural networks. Starting with a leaner, potentially pretrained model can help reduce large outlays on hardware and cooling solutions, as they can run on hardware you may already own.
• Energy efficiency: AI GPUs have made strides in power efficiency through software optimizations and a reduced memory footprint. Alternatively, other types of AI processors, including FPGAs and CPUs with built-in AI accelerators, may provide improved energy consumption for industry-specific workloads.

GPU for AI Solutions

GPUs are relied on to supercharge AI in virtually every type of compute infrastructure. They are used in public and private data centers, at the edge, and in hybrid and traditional computing environments where they are slotted into server racks, nodes, and individual workstations:

• In the data center, GPUs are used to process workloads that are large in scale or have high power requirements, such as extracting information from a large collection of video footage. They are also used to perform resource-intensive workloads, like training and data analytics, and to process data collected from multiple edge sources when latency is not a concern.
• At the edge, discrete GPUs may be ideal for use cases requiring high performance and complex model support. They are commonly used for inference tasks like monitoring camera imagery or coordinating complex robotic movements in a warehouse. They also play a role in hybrid edge approaches, in which workloads are distributed between the edge and the data center. Fast and lightweight processors can generate near-real-time insights at the edge, while data center GPUs provide deeper context on data transmitted to the cloud. The hybrid edge helps to conserve bandwidth, improve latency, increase security, and ensure data compliance.
• In an offline or air-gapped environment, an AI-capable workstation can be used to aid in research and development, speed time to market, and accelerate scientific discovery.