What Does a GPU Control? More Than Just Graphics
By
Ethan Fahey
•
Sep 4, 2025
A GPU, or Graphics Processing Unit, is essentially a powerhouse chip built to handle the heavy lifting of rendering images and videos. While they were created to make graphics look sharper and run smoother, GPUs have since evolved into critical tools for much more: powering AI, driving scientific research, and accelerating complex computations across industries. In this article, we’ll break down what GPUs are, how they’ve evolved, and why they matter in today’s tech-driven world. And for businesses looking to harness AI effectively, platforms like Fonzi AI make it easier to leverage this kind of cutting-edge computing power to streamline recruiting and engineering workflows.
Key Takeaways
GPUs have evolved from solely graphics rendering tools to versatile processors capable of accelerating a wide range of computational tasks, including AI, scientific research, and video editing.
The architecture of GPUs allows for parallel processing, making them highly efficient for tasks that require extensive calculations, surpassing the capabilities of traditional CPUs in specific applications.
Different types of GPUs, including integrated, discrete, and virtual, cater to diverse user needs, each providing unique advantages in performance, power efficiency, and scalability.
What is a GPU?
A Graphics Processing Unit (GPU) is a specialized processor designed initially to improve the efficiency of graphics rendering. Approximately 20 years ago, GPUs mainly served to enhance real-time 3D graphics applications, including video games. This advancement enabled smoother animations and created more immersive experiences for users. The advent of GPUs marked a significant leap forward in computer graphics, enabling more detailed and visually appealing game environments, particularly with the use of graphics cards and graphics processors.
However, modern GPUs have transcended their original purpose. They now offer the flexibility to accelerate a broad range of applications beyond traditional graphics rendering. From rendering hyperrealistic in-game worlds to performing complex computations for scientific research, today’s GPUs are multifaceted tools meeting the demands of diverse, computationally intensive tasks. This evolution is a testament to the remarkable versatility and power of GPU technology.
How Does a GPU Work?
At the heart of a GPU lies its ability to perform parallel processing. Unlike a Central Processing Unit (CPU), which excels at handling a few processing tasks at a time, GPUs consist of thousands of smaller, specialized cores that can handle multiple tasks simultaneously, even with fewer cores. This architecture makes them particularly adept at tasks that require parallel processing, such as rendering graphics and performing complex mathematical computations, including processing units.
One of the critical components of a GPU is its video memory system. GPUs utilize a specialized type of RAM known as GDDR6, which is optimized for transferring large amounts of graphical data quickly. GDDR6 memory offers higher bandwidth and faster speeds compared to its predecessors, significantly enhancing overall GPU performance.
High-performance tasks like 4K gaming and real-time ray tracing use an advanced version called GDDR6X, which features improved data transfer efficiency, further boosting maximum performance in demanding applications with high-performance GPUs and ray tracing cores.
The memory bandwidth of GDDR6 can reach up to 16 Gbps, whereas GDDR6X can achieve a maximum of 21 Gbps per pin. Such capabilities enable GPUs to handle vast amounts of data with impressive speed and efficiency, making them indispensable for high-performance computing tasks. Whether it’s rendering detailed graphics or crunching numbers for a machine learning model, the advanced memory technology in modern GPUs plays a crucial role in their exceptional performance.
Types of GPUs
GPUs come in various forms, each tailored to meet different computing needs. They can be broadly categorized into three main types: integrated, discrete, and virtual GPUs. Each type has its unique advantages and use cases, making them suitable for different applications and user requirements.
Integrated GPUs
Integrated Graphics Processing Units (IGPUs) are built in GPUs that are built into the computer’s CPU and use a portion of the system’s RAM for memory. They are most commonly found in laptops and budget desktops due to their cost-effectiveness and power efficiency. Integrated GPUs provide a good balance between performance and power consumption, making them an ideal choice for everyday computing tasks and light gaming, especially when considering integrated GPU application-specific integrated circuits.
In early 2007, integrated graphics in computers made up approximately 90% of all PC shipments, highlighting their dominance and importance for users who do not require the high performance of discrete GPUs or video cards.
Integrated GPUs are also crucial in maintaining a compact form factor for devices, contributing to the sleek designs of modern laptops and ultrabooks.
Discrete GPUs
Discrete GPUs, unlike their integrated counterparts, are independent graphics processing units with their dedicated memory. This independence allows them to deliver superior performance, which is why they are often preferred in gaming and professional applications. Discrete graphics GPUs are capable of handling more demanding tasks, rendering high-definition graphics, and supporting advanced features like real-time ray tracing.
Their dedicated graphics card memory and superior processing power make discrete GPUs the go-to choice for content creators and gamers. Whether it’s for rendering 3D models, editing high-resolution videos, or playing the latest AAA games, discrete GPUs provide the necessary horsepower to ensure smooth and efficient performance.
Virtual GPUs
Virtual GPUs are designed to provide scalable graphics processing power for cloud computing environments. They enable multiple users to share the same physical GPU resources, enhancing resource allocation and cost efficiency. Businesses can optimize their computing resources with virtual GPUs, making GPU power available where and when it’s needed most.
In cloud environments, virtual GPUs play a crucial role in enabling scalable and flexible computing solutions. They are particularly beneficial for applications that require significant parallel processing capabilities, such as deep learning, complex mathematical operations, and cloud GPU large-scale simulations.
Virtual GPUs ensure that users can access powerful computing resources without the need for physical hardware, making them an integral part of modern high-performance computing and GPU computing solutions.
GPU vs. CPU
The battle between GPUs and CPUs is a tale of two processors designed for different purposes. Central Processing Units (CPUs) are designed to process data for a wide range of tasks and excel in applications where quick response times to individual operations are crucial. They manage the operating system, run applications, and perform tasks that require low latency and versatility, leveraging multiple CPU cores and the computer’s CPU.
On the other hand, GPUs are optimized for rendering graphics and performing computations that can be executed in parallel. Their architecture includes multiple Streaming Multiprocessors (SMs) that execute arithmetic operations simultaneously, significantly enhancing performance. This capability, along with their GPU capabilities, makes GPUs particularly adept at handling tasks that require extensive parallel processing, such as graphic rendering and complex simulations.
In summary, while CPUs are ideal for general-purpose tasks and quick response times, GPUs excel in parallel processing, enabling faster computation for graphics rendering and complex mathematical calculations. The complementary roles of CPUs and GPUs highlight their importance in modern computing, with each playing a vital part in delivering high-performance and efficient computing solutions.
Modern Use Cases of GPUs
Modern GPUs have come a long way from their origins in 3D graphics rendering. They now support a wide array of complex computational tasks. One of the most significant areas where GPUs have made an impact is in artificial intelligence (AI) and machine learning. Their ability to perform extensive calculations simultaneously makes them ideal for training AI models, handling large datasets, and managing AI workloads.
In scientific research, GPUs accelerate tasks such as climate modeling and drug discovery by processing large datasets quickly. They are also instrumental in financial technology, enabling rapid data analysis and high-frequency trading. The versatility of GPUs extends to edge computing applications, such as autonomous vehicles handling data-intensive camera feeds for immediate decision-making. This is where the GPU excels.
Beyond these applications, GPUs are highly programmable, allowing them to be utilized for many tasks beyond traditional graphics. From video production to deep learning, modern GPUs handle various types of data simultaneously, making them advantageous for computationally demanding tasks. Their role in AI, machine learning, and scientific research underscores their indispensable place in today’s technological landscape.
The Importance of GPU Performance
GPU performance is a critical factor in determining the efficiency and speed of computational tasks. It is typically measured in floating-point operations per second (FLOPS), often in teraflops (TFLOPS). Several factors affect GPU performance, including:
The size of connector pathways
Clock signal frequency
On-chip memory caches
The number of streaming multiprocessors (SM) or compute units (CU).
Memory bandwidth, which refers to the speed at which data can be read from or written to the GPU’s memory, also plays a crucial role in graphics performance. Performance can be limited by power draw and heat dissipation, making power efficient energy efficiency an essential consideration.
Launching multiple thread blocks, typically four times the number of available SMs, ensures that the GPU can handle high-intensity tasks effectively, maximizing its computational power and efficiency through parallel computing.
What Does a GPU Control? More Than Just Graphics
GPUs have evolved to perform tasks far beyond graphics rendering. In video editing, GPUs enable real-time manipulation of high-resolution footage, significantly reducing rendering times and enhancing the creative process for professionals. Their parallel processing capabilities make them suitable for complex simulations and mathematical calculations, allowing thousands of simple calculations to be performed simultaneously.
In the realm of quantum computing, GPUs help researchers model quantum algorithms and systems, paving the way for future advancements. Data center GPUs enhance operations for parallel tasks, including AI, media analytics, and 3D rendering. This versatility underscores the critical role GPUs play in various high-performance computing applications.
Beyond these applications, GPUs are instrumental in:
Training AI models
Performing complex mathematical calculations
Video editing
Quantum computing simulations
Their ability to handle diverse tasks efficiently makes them indispensable in modern computing environments. GPUs control a wide range of functions that extend far beyond graphics rendering.
Introduction to Fonzi
Fonzi is a curated AI engineering talent marketplace that connects companies to top-tier, pre-vetted AI engineers through its recurring hiring event, Match Day. Unlike traditional job boards or black-box AI tools, Fonzi delivers high-signal, structured evaluations with built-in fraud detection and bias auditing.
This approach ensures an optimal balance in a fair and efficient hiring process, significantly reducing the time taken to fill roles.
How Fonzi Works
Fonzi’s Match Day events connect pre-vetted AI engineers with companies through structured hiring events, greatly streamlining the recruitment process. During these events, employers can extend real-time, salary-backed job offers to chosen candidates within a 48-hour timeframe. This accelerated process ensures that companies can secure top talent quickly and efficiently.
The platform includes a curated marketplace that features only qualified candidates, enhancing the quality and readiness of the talent pool. Fonzi’s structured evaluations feature mechanisms for fraud detection and bias auditing, ensuring fair candidate assessments. This commitment to a bias-free hiring process fosters a positive and inclusive environment for all candidates.
Utilizing AI applications in Fonzi’s recruitment process helps streamline candidate evaluations and improve overall efficiency. Providing a single application that connects candidates to multiple vetted job offers, Fonzi enhances the hiring experience, ensuring a better overall journey for job seekers.
Why Choose Fonzi for Hiring AI Engineers
Fonzi’s approach allows startups to efficiently hire AI engineers, significantly reducing the time taken to fill roles, often to just three weeks. This efficiency is achieved through:
Automated screening
Bias-audited evaluations, ensuring a fair and efficient hiring experience
Built-in fraud detection mechanisms to ensure the authenticity of candidates during the hiring process.
Supporting both early-stage startups and large enterprises, Fonzi accommodates hiring from the first AI hire to the 10,000th. This versatility makes it an ideal solution for companies at any stage of growth. Fonzi preserves and elevates the candidate experience, ensuring engaged, well-matched talent.
Fonzi allows companies to tap into a curated pool of top-tier AI engineering talent, streamlining their recruitment process and ensuring they hire the best candidates available. The platform’s focus on quality and efficiency makes it a valuable resource for any organization looking to bolster its AI capabilities.
Summary
In summary, GPUs have evolved from their initial role in graphics rendering to become indispensable tools in various high-performance computing applications. Their ability to handle complex computations, train AI models, and accelerate scientific research underscores their importance in today’s technological landscape. The different types of GPUs, including integrated, discrete, and virtual, cater to a wide range of computing needs, making them versatile solutions for various applications.
Fonzi, a curated AI engineering talent marketplace, offers a fast and efficient hiring process for companies looking to recruit top-tier AI engineers. By leveraging structured evaluations, fraud detection, and bias auditing, Fonzi ensures a fair and efficient recruitment process, accommodating the needs of both startups and large enterprises. As we continue to push the boundaries of technology, the role of GPUs and platforms like Fonzi will undoubtedly remain pivotal in shaping the future of computing.
