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What is a GPU in AI?
Why do AI use GPU instead of CPU?
What GPU is used to train AI?
This blog provides a clear explanation of why GPUs are fundamental to modern AI, detailing how these graphics processing units power rapidly evolving AI models. It breaks down how AI accelerators, including NVIDIA GPUs, manage demanding tasks in machine learning, deep neural networks, and generative AI.
Confused about why AI needs such powerful hardware?
As AI models grow larger, regular chips can’t keep up. That’s where GPUs come in. Originally built for gaming, they now lead the charge in machine learning , deep learning, and generative tools. This article explains how they do it—and why tech companies invest heavily in them.
Also, we’ll look at how AI accelerators like NVIDIA GPUs handle tasks that traditional CPUs can’t manage as efficiently. If you’ve heard that data centers are packed with GPUs, there’s a reason. From training deep neural networks to supporting real-time results, this hardware keeps things moving fast.
If you're building an app, scaling a platform, or simply curious about how all this works, you're in the right place. Let’s break down the real role of a GPU artificial intelligence setup so you can finally see the buzz.
Keep reading.
Originally designed for rendering graphics, graphics processing units (GPUs) are now integral to artificial intelligence . Their parallel architecture, with thousands of relatively small cores, lets them process many tasks simultaneously, which is ideal for neural networks and deep learning applications.
Traditional central processing units (CPUs) handle a few threads efficiently. In contrast, modern GPUs can run thousands of parallel threads—perfect for the demands of ai workloads.
| Feature | Central Processing Unit | Graphics Processing Unit |
|---|---|---|
| Cores | 4–32 powerful cores | Thousands of lightweight cores |
| Strength | Sequential task execution | Massive parallel processing |
| Best For | General-purpose computing | Training large ai models |
Without this architecture, many modern AI applications, like large language models or video processing pipelines, would be impossible to train practically.
AI is fundamentally about number-crunching power. GPUs deliver this with extreme throughput and memory bandwidth, making them ideal for running deep neural networks or performing scientific simulations.
The larger the ai model, the more ai accelerators are required to handle it efficiently.
Here's a simple Mermaid diagram showing how data flows during deep learning training:
GPUs excel at parallel matrix multiplication, a core operation in machine learning tasks.
A typical modern CPU may take hours for what a GPU can do in minutes.
Many data centre GPUs operate together to support even larger AI models across data centers.
While traditional GPUs started the trend, today’s AI accelerators typically go further. These include specialised processors like neural processing units, AI chips, and other AI accelerators.
Designed for specific machine learning algorithms
Reside on the same chip package as memory for faster data transfer
Capable of specialised functions like tensor computations
AI accelerators typically sacrifice general-purpose features for raw throughput in ai workloads and generative ai systems.
These specialised chips are used in data centers, integrated into large desktop computers, and embedded systems. They allow for providing specialised processing without a single very large accelerator.
The more complex AI models become, the more expensive specialised hardware is needed to train them. Designing and fabricating these chips requires considerable engineering resources.
Running AI workloads at scale demands many data centre GPUs, which consume massive amounts of electricity and require cooling. This drives up operating costs, adding to why so expensive specialised hardware dominates AI training.
Even with the best hardware, more complex software is needed to fully extract performance, especially for a given machine learning algorithm. This often requires custom toolchains, compilers, and frameworks.
AI applications span from machine learning to scientific computing, relying on GPUs and AI accelerators.
| Use Case | AI Role | Hardware Used |
|---|---|---|
| Self-driving cars | Interpret sensor data using neural networks | AI accelerators, GPUs |
| Medical imaging | Analyze patterns in scans | Specialised processors, data centre gpus |
| Social media | Filter and rank content using deep neural networks | Large gpu like accelerators |
| Finance | Predict market trends using machine learning algorithms | AI chips, specialised accelerators |
The AI boom has made GPU resources a commodity. The larger the AI model, the more GPUs or specialised accelerators are required to support it.
GPUs generate graphical scenes and train models with immense heat output. Cooling becomes a major bottleneck, especially in large desktop computers or data centers.
Training deep neural networks requires syncing updates across many data centre GPUs, introducing latency and bandwidth constraints.
The AI capabilities enabled by GPUs are impressive, but the costs, from specialised functions to expensive specialised hardware, can be prohibitive.
The demand for ever more specialised accelerators is only increasing. As larger AI models become common, specialised chips will coexist with traditional GPUs. Expect to see:
Chips optimized for specific machine learning algorithms
High-performance computing platforms blending CPUs, GPUs, and neural processing units
More integration between AI technologies and hardware layers within the same chip package
GPU artificial intelligence is no longer limited to gaming or graphics. It now forms the computational backbone of modern AI applications , powering machine learning, deep learning, and scientific computing at scale. From parallel processing to running large language models, GPUs and AI accelerators have become indispensable in pushing forward what machines can learn and do.
As many modern ai applications continue to expand in ambition and size, expect GPUs—and their specialised cousins—to remain central to training large ai models and achieving next-gen ai capabilities.
