What is a GPU? A comprehensive overview

In recent years, Graphics Processing Units (GPUs) have become increasingly important, not only for gaming and graphic rendering, but also for tasks like artificial intelligence, machine learning, and scientific computing. As technology advances, the role of GPUs continues to expand across industries.

This article provides a comprehensive overview of what a GPU is, how it works, its different types, and the applications it powers today.

History of GPUs

GPUs were first introduced in the late 1990s to handle complex graphics rendering for video games and multimedia applications. The first widely recognized GPU was NVIDIA's GeForce 256, released in 1999. It brought a paradigm shift in the gaming industry by offloading graphical tasks from the CPU.

Over the years, GPUs have evolved far beyond gaming. As their parallel processing capabilities improved, they found new use cases in scientific computing, data analysis, and artificial intelligence. Companies like NVIDIA and AMD have continuously pushed the limits of GPU technology, enabling them to handle tasks like machine learning, deep learning, and complex simulations.

Most recently, the rise of large language models (LLMs) has significantly increased the demand for GPUs. These models require massive amounts of data and computational power to train, and GPUs are ideal for these tasks. NVIDIA, in particular, has experienced tremendous growth due to its dominance in the GPU market, which is directly attributable to the increasing demand for AI and machine learning applications.

How do GPUs work? How are they different from CPUs?

GPUs are designed with a large number of smaller, efficient cores that are great at performing multiple tasks at the same time. This architecture is different from CPUs, which have fewer cores optimized for sequential, complex tasks. For general-purpose tasks, CPUs are typically a good fit, but for repetitive, parallel tasks, GPUs take the lead.

The thousands of cores within a GPU can work together to process large blocks of data at once. Each core is capable of handling a small part of a bigger problem, which allows GPUs to break down tasks and work on them in parallel. This design makes them excel at complex tasks like rendering images, running simulations, or training machine learning models.

For better understanding, here’s a simplified overview of how a GPU would go about training a model:

1. The GPU loads the training data into its memory. The data is normalized to a common scale so that features are treated equally. If applicable, the data is augmented to create variations and improve model generalization (e.g., flipping images, rotating, cropping).
2. The weights and biases of the neural network are initialized with random values. The GPU sets up the neural network architecture, including the number of layers, neurons per layer, and activation functions.
3. The GPU divides the training data into batches. Each batch is assigned to a different group of cores. The input data is passed through the neural network layers, and features are extracted. The final layer produces output predictions.
4. The difference between the predicted outputs and the actual target values is calculated using a loss function (e.g., mean squared error, cross-entropy).
5. The loss that’s calculated is subsequently propagated back through the network. The loss’s gradients are calculated with respect to the weights and biases.
6. An optimization algorithm (e.g., gradient descent, Adam) is used to update the weights and biases based on the gradients.
7. Steps 3–6 are repeated for multiple epochs. With each epoch, the model's ability to learn from the data and make accurate predictions improves.
8. The trained model is evaluated on a separate validation data set to assess its performance. If necessary, the model's hyperparameters can be adjusted and the training process can be repeated.

In this example, GPUs are the obvious choice over CPUs because their parallel processing capabilities and optimized matrix operations drastically reduce training time.

When do you need GPUs?

Whenever there’s a need to process large-scale data quickly, perform repetitive mathematical tasks, or render complex graphics, GPUs are the best tool for the job. Let’s discuss some of their top use cases:

Gaming and graphics rendering

One of the earliest and most well-known uses for GPUs is in gaming. GPUs are great at rendering complex graphics, which enables smooth gameplay and realistic visuals. Modern video games rely heavily on GPUs to process textures, lighting, and visual effects in real time. GPUs are also widely used in 3D modeling, animation, and video editing, where they help render high-resolution content much faster than a CPU could manage.

Artificial intelligence and machine learning

GPUs play a central role in AI and ML by speeding up the training of deep learning models. Their massive parallel processing power allows them to handle large data sets and perform multiple calculations at scale. This feature is crucial for tasks like image recognition, natural language processing, and autonomous driving.

Data science and high-performance computing

In fields like data science, physics simulations, and scientific research, GPUs are used to accelerate computations. For example, researchers use GPUs to model complex systems like climate patterns, molecular interactions, or space simulations. Their design enables them to analyze big data in a fraction of the time.

Video encoding and streaming

GPUs are also widely used for video encoding and real-time streaming. They handle tasks like video transcoding and compressing high-resolution content, which enables streaming platforms to function. The GPU's efficiency in processing frames and optimizing image quality makes it a key component for video production and media services.

Cryptocurrency mining

GPUs are heavily used in cryptocurrency mining, particularly for coins like Ethereum. Mining involves solving complex mathematical puzzles to validate transactions on the blockchain, a process that requires immense computing power. GPUs, with their ability to calculate at scale and in parallel, are much more efficient than CPUs for this type of task.

Virtual Reality (VR) and Augmented Reality (AR)

VR and AR applications require significant graphical power to provide immersive experiences. GPUs are responsible for rendering 3D environments and managing the high frame rates needed for smooth interaction in virtual spaces. Without a powerful GPU, VR headsets and AR applications would suffer from lag and poor visual quality.

GenAI and GPUs

GPUs have become a key player in powering Generative AI (GenAI) applications. GenAI is all about the creation of new content — like images, text, and music — using deep learning models. These models, such as GPT or DALL-E, require an immense amount of computational power, and at this time, there is no better computing architecture than GPUs to handle the workload.

Why are GPUs so popular in GenAI?

Let’s explore why GPUs are a central component of all GenAI workloads:

• GenAI models like transformers, used in NLP and image generation, involve complex computations on vast amounts of data. GPUs can handle the thousands of matrix multiplications and other operations required by these models in parallel. This speeds up training and inference, which in turn makes it possible to generate content in real time.
• Deep learning models, particularly for GenAI, require a lot of memory to store and process data. GPUs offer high memory bandwidth, meaning they can quickly read and write large amounts of data to and from memory.
• GenAI models are dependent on tensor operations — mathematical computations that involve multi-dimensional arrays. GPUs are specifically optimized for these operations, which are core to the architecture of models like GPT-4 or Stable Diffusion.
• As GenAI models grow larger and more complex, they need scalable computing power. GPUs can be used in clusters, allowing multiple GPUs to work together and scale the performance of AI workloads. This capability makes it possible to train, deploy, and scale even the most massive models, such as those used in large-scale language generation.

Types of GPUs

Here’s a breakdown of the major types of GPUs:

Integrated GPUs

Integrated GPUs are built directly into the CPU — i.e., they share the same chip. These GPUs don’t have dedicated memory and use the system’s RAM to process graphics. They are found in most budget laptops and desktops, and are designed for basic tasks like web browsing, video playback, and light gaming.

• Advantages: Lower cost, lower power consumption.
• Limitations: Limited performance; not suitable for high-end gaming or intensive workloads.

Discrete GPUs

Discrete GPUs, also known as dedicated GPUs, are standalone graphics cards that come with their own dedicated memory (VRAM). They offer a lot more power than integrated GPUs, and are used in systems that require better graphics performance.

• Advantages: Higher performance, dedicated VRAM, better handling of resource-intensive tasks.
• Limitations: Higher cost, more power consumption, generates more heat.

Gaming GPUs

Gaming GPUs are a subcategory of discrete GPUs designed specifically for high-end gaming. They provide the power needed to run the latest games at high frame rates, often with advanced features like real-time ray tracing and enhanced shaders for realistic visuals.

• Advantages: Optimized for gaming, high frame rates, real-time ray tracing.
• Limitations: Expensive, can be excessive for non-gaming applications.

Professional GPUs (aka workstation GPUs)

Professional GPUs are designed for workloads like 3D modeling, video editing, CAD (Computer-Aided Design), and scientific simulations. These GPUs are built for stability and accuracy over long periods of intensive use. They often come with additional certifications for performance in specific professional software like AutoCAD, Adobe Premiere, or Blender.

• Advantages: Precision, stability, optimized for professional software, large amounts of VRAM.
• Limitations: Very expensive, not ideal for gaming or day-to-day use cases.

Mobile device GPUs

Mobile GPUs are built into smartphones, tablets, and other mobile devices to handle graphics rendering for games, apps, and user interfaces. These GPUs are optimized for power efficiency since mobile devices rely on battery life. While not as powerful as desktop GPUs, modern mobile GPUs can still handle demanding smartphone games and apps.

• Advantages: Power-efficient, compact design, optimized for mobile.
• Limitations: Limited performance compared to desktop GPUs.

Choosing the right GPUs for your workload

When choosing a GPU, you need to consider several factors, most of which are covered here:

Purpose of use

Begin by understanding the primary tasks you'll perform with the GPU. Different workloads require varying levels of performance. For example:

• Basic tasks: If you’re doing everyday computing (web browsing, video playback, office work), an integrated GPU will suffice. These are cost-effective and use less power.
• Gaming: For gamers, a discrete gaming GPU with higher VRAM and clock speeds is usually important. Look for GPUs that support the latest game technologies like real-time ray tracing and high frame rates.
• Professional work: For 3D modeling, video editing, or machine learning, a workstation GPU is the better choice. These are optimized for heavy computational tasks and offer more stability and precision.

Performance and VRAM

Graphics performance and video memory (VRAM) are important considerations. Here’s a basic guide:

• Casual use: 2–4 GB of VRAM is sufficient for general tasks or light gaming.
• Gaming: For 1080p gaming, 6–8 GB of VRAM is recommended, while for 4K gaming or VR, you’ll need 8–12 GB of VRAM for smoother performance.
• Professional tasks: Rendering, video editing, and machine learning require more VRAM. Look for GPUs with 12 GB or more if you’re dealing with large data sets, high-resolution media, or complex simulations.

Budget

Your budget will largely dictate what kind of GPU you can afford. It's important to strike a balance between price and performance.

• Low budget: Integrated GPUs or entry-level discrete GPUs are suitable for casual users on a budget.
• Mid-range: For moderate gaming or creative work, consider mid-range GPUs.
• High-end: If your workload includes professional tasks or AAA gaming at high resolutions, consider high-end GPUs like the GeForce RTX 4090.

Power consumption and cooling

GPUs can vary greatly in their power consumption and cooling requirements.

• Low power requirements: Integrated and mobile GPUs use less power, so they are ideal for laptops or small form-factor PCs where energy efficiency is important.
• High power requirements: High-end gaming and workstation GPUs require more power, sometimes over 300 watts. You’ll need to ensure that your power supply and cooling system can handle the additional heat and energy.

Form factor and space

Consider the physical size of the GPU and whether it will fit in your system.

• Small builds: If you’re building a compact system, look for low-profile or mini GPUs. These are designed to fit smaller cases while still offering decent performance.
• Full-sized PCs: For larger gaming rigs or workstations, you can opt for full-sized GPUs, but make sure your case has enough space and ventilation.

Software compatibility

Ensure that the GPU you choose is compatible with the software or applications you plan to use.

• Gaming: Most modern games are optimized for NVIDIA or AMD GPUs.
• Professional software: Certain professional applications, such as AutoCAD or Adobe Premiere, may perform better on workstation GPUs like NVIDIA’s Quadro series, which come with certifications for stability and optimization with these tools.

Multi-GPU setups

Some tasks, like AI training or advanced rendering, may require multiple GPUs. For example, if you are doing machine learning or 3D rendering, you may benefit from a multi-GPU setup to spread the workload across several GPUs. But make sure that your system and software support this configuration.

Future-proofing

Think about whether your GPU will still meet your needs in a few years. Generally, mid-range to high-end GPUs provide more headroom for future workloads, especially if you plan on gaming at higher resolutions or working with increasingly complex data.

Monitoring GPU performance

To ensure that your GPU is performing at the highest levels, it’s important to keep an eye on various metrics.

GPU utilization

This measures the percentage of GPU processing power being used. A high utilization rate (near 100%) indicates that the GPU is working at full capacity, while low utilization may mean it’s underused.

What to look for: High utilization during intensive tasks (gaming, rendering, AI training) is normal. Consistently low utilization could suggest the task is not GPU-bound or is being bottlenecked elsewhere (e.g., CPU, memory).

VRAM usage

VRAM (Video RAM) stores data that the GPU is processing, such as textures, models, and frames. VRAM usage shows how much memory is in use.

What to look for: High VRAM usage during tasks like gaming, 3D rendering, or AI tasks is common. If you exceed your available VRAM, performance may drop, and the system will rely on slower system RAM.

GPU temperature

Temperature is a measure of how hot your GPU is running. GPUs naturally produce heat, but high temperatures can throttle performance or damage components over time.

What to look for: Ideal GPU temperatures under load typically range from 65°C to 85°C. If temperatures exceed 90°C consistently, it may be a sign of cooling issues that need attention.

Clock speed

The clock speed of your GPU’s core and memory determines how fast it processes instructions and transfers data. Generally, the higher the clock speed, the better the performance.

What to look for: Ensure that clock speeds are stable under load. If you notice sudden drops in speed, it could be a sign of thermal throttling due to overheating.

Frame rate (FPS)

FPS measures how many frames per second your GPU can render during gameplay or video playback. Higher frame rates result in smoother visuals.

What to look for: Stable FPS that matches your monitor’s refresh rate (e.g., 60 FPS for a 60 Hz monitor) is ideal for a smooth experience. Significant FPS drops can be due to a GPU bottleneck.

Render time

Render time is the amount of time it takes for the GPU to render each frame. Lower render times indicate better performance.

What to look for: Lower render times are better, especially for demanding tasks like gaming or video rendering. If render times increase, it might be time to upgrade your GPU.

Latency

Latency refers to the delay between input and the time the GPU processes it. This is especially important for gaming or real-time applications like VR.

What to look for: Lower latency ensures quicker response times and smoother experiences, especially in competitive gaming. High latency may indicate a need for system optimization.

To track all these key metrics (and more) in real time, you can use dedicated monitoring tools, such as the GPU Monitoring Solution by Site24x7. This tool requires minimal setup and ensures that you have complete and continuous visibility into your GPU’s performance.

Conclusion

GPUs are a fundamental component of many computing use cases, including gaming, rendering, and artificial intelligence. We hope that the insights in this guide have deepened your understanding of GPUs and will help you make the most of their potential for your specific needs.