As demand for speed and data processing explodes, GPUs are becoming essential for unlocking the potential of next-generation technologies like AI and edge computing.
Graphics processing units (GPUs) are defined as specialized electronic circuits that can process large amounts of data simultaneously. They might have tens, hundreds or thousands of connected cores that support “parallel processing,” or the running of multiple tasks at once. This allows them to move more quickly than traditional CPUs, making them ideal for more complex, data-intensive workloads such those associated with artificial intelligence. .
GPUs were initially designed to support modern gaming applications, which require fast, responsive, performance, high-definition graphics and video. But their capabilities translate well to other areas including high performance computing (HPC), AI, marching learning (ML), deep learning and other computationally demanding tasks that can overwhelm traditional processors.
How GPUs are different from CPUs
GPUs and CPUs are both computer and server hardware components. But whereas GPUs are ideal for comprehensive, data-heavy tasks, CPUs act as a “brain” of sorts that manage computer systems. They have been around for decades and are an essential component of today’s computers; at the simplest level, they are what allow computers to function.
CPUs operate by processing tasks sequentially and typically contain far fewer cores than GPUs. They handle more general-purpose tasks than GPUs, such as running applications, performing input and output operations and processing graphics. They can process some more complex tasks, as well, such as lower-level AI workloads (like inferencing, or model training).
It’s important to note that GPUs and CPUs work together, each complementing the other based on the tasks they excel at.
How GPUs work
GPUs are built on silicon wafers, which serve as the base for a number of smaller, more specialized multiprocessors. These are connected by transistors that allow them to communicate, perform calculations and perform several tasks at the same time (hence: parallel processing). Tasks are parceled out into smaller, independent steps that are distributed across the GPU’s architecture.
GPUs typically have their own RAM so they can store, access and alter the data they’re processing faster than CPUs would be able to do on their own. This RAM is specifically designed to handle large amounts of data for highly intensive compute cases, allowing GPUs to be fast, multitask, handle large files,and perform more memory-intensive tasks.
Different types of GPUs
GPUs can be offered as standalone chips (discrete GPUs), integrated with other computing hardware (integrated GPUs), or as virtual (vGPUs)/cloud GPUs.
Discrete GPUs are distinct chips separate from a device’s CPU. This means they have their own memory that is not shared with the processor, allowing for higher performance. They are typically dedicated to specific tasks with special requirements and performance demands, such as graphics and content creation. However, there’s a trade-off in increased energy consumption; standalone chips also create much more heat and require specialized cooling capabilities.
Integrated GPUs comprise the majority of GPUs on the market today. They are built directly into processors and share system memory with the CPU. This typically allows for thinner, lighter systems with reduced system costs and power consumption.
vGPUs are software-based GPU clusters offered by cloud service providers (CSPs). This type of virtual infrastructure is growing in popularity, as it doesn’t require enterprises to purchase and maintain their own physical hardware.
Applications for GPUs
Although they were originally intended to handle the intensive compute workloads of next-gen gaming, GPUs are incredibly useful for a growing number of other tasks.
High-performance computing
High-performance computing (HPC) is a perfect use case for GPUs. It employs clustering, or connected groups of computers working together as a single unit to perform complex calculations at high speed. HPC offers performance gains well above those of single computers, workstations or servers, and it is powered by the parallel processing capabilities of GPUs.
HPC is the engine behind supercomputers, machines with immense processing power. Supercomputers are critical to computational science, supporting fields including quantum mechanics and quantum computing, deep data analysis, physical simulations, weather forecasting and other fields of science.
HPC is also essential for data analysis, as it allows data scientists to identify patterns and parallels in data that would be otherwise difficult to detect.
Machine learning and deep learning
ML and deep learning (DL) serve as the backbone of data science; models sift through massive datasets and use algorithms to essentially simulate how humans learn. But they require a significant amount of compute power.
GPUs can accelerate ML capabilities, allowing models to more optimally process datasets, identify patterns and draw conclusions. Because they can perform many calculations at once, GPUs also enhance model memory and optimization.
AI and generative AI
Today’s increasingly sophisticated AI technologies — notably large language models (LLMs) and generative AI — require lots of speed, lots of data and lots of compute. Because they can perform simultaneous calculations and handle vast amounts of data, GPUs have become the powerhouse behind AI (e.g., AI networking and AI servers).
Notably, GPUs help train AI models because they can support complex algorithms, data retrieval and feedback loops. In training, models are fed huge datasets — broad, specific, structured, unstructured, labeled, unlabeled — and their parameters adjusted based on their outputs. This helps to optimize a model’s performance, and GPUs help to accelerate the process and get models more quickly into production.
But a GPUs’ work doesn’t stop there.Once models are put into production, they need to be continuously trained with new data to improve their prediction cap abilities (what’s known as inference). GPUs can execute ever more complex calculations to help improve model response and accuracy.
Edge computing and internet of things (IoT)
GPUs are increasingly critical in edge computing, which requires data to be processed at the source – that is, at the edge of network. This is important in areas such as cybersecurity, fraud detection and IoT), where near-instant response times are paramount.
GPUs help to reduce latency (compared to sending data to the cloud and back), lower bandwidth (transmitting large amounts of data over networks is not necessary) and enhance security and privacy measures (the edge keeps data local).
With GPUs as their backbone, edge and IoT devices can perform object detection and real-time video and image analysis, identify and flag critical anomalies and perform predictive maintenance, among other important tasks.
Top GPU providers today
There are numerous companies competing in the GPU and chips market today. Top providers include the following:
Nvidia
Nvidia is one of the highest-valued companies in the world, offering tools across the hardware and software stacks. Its top products include Nvidia Blackwell (an “AI superchip”), the Hopper accelerated computing platform, the Ada Lovelace microarchitecture for AI-based neural graphics and Tensor Cores for deep learning tasks, as well as virtual GPU offerings.
Marvell
Marvell started out in consumer electronics, but it has increasingly expanded into custom silicon — integrated circuits or chips — specifically designed for particular customers or applications. Its wide range of integrated circuits are in networking, storage and data centers (as well as consumer electronics).
Broadcom
Broadcom is known for its networking and communications chips, but is also becoming a formidable player in custom AI chips (ASICs) used in data centers and the cloud. Like Marvell’s offerings, these are also built for specific workloads.
Intel
Intel is known for its offerings in enterprise servers and PCs, but it also builds integrated circuits, network interface controls and motherboard chipsets.
AMD
Advanced Micro Devices (AMD) produces CPUs and GPUs, field-programmable gate arrays (FPGAs) (off-the-shelf integrated circuits), system-on-chip (SoC) (integrated circuits combining multiple system components) and HPC tools.
Samsung
Samsung is known for its cellular devices, but it also manufactures memory chips and advanced logic chips. It also operates a foundry, meaning it manufactures chips for other companies, as well.
Qualcomm
Qualcomm’s Snapdragon chipsets are found in many mobile devices. Its Cloud AI 100 line is designed to accelerate AI inference, and its application processors power IoT applications.
GPUs: Looking ahead
GPUs are playing an increasingly pivotal role in modern computing — and likely will continue to for the foreseeable future — as enterprises use HPC, ML, AI and edge environments to stay competitive. Working alongside existing tools such as CPUs, they can dramatically increase speed and performance and help unlock the incredible value of next-generation technologies.
