What is an NVIDIA GPU? NVIDIA GPU Guide

To fully understand the question “What is an NVIDIA GPU?”, it is important to define both concepts first. Continue reading to learn what NVIDIA—a California-based technology company—and the GPU—one of the core components of modern computers—actually mean. Here is everything you need to know about NVIDIA GPUs!

In response to the question “What is NVIDIA?”, it can be defined as a technology company that designs graphics processors for online gamers, mobile devices, and system-on-chips. NVIDIA’s GeForce product family directly competes with AMD’s globally known Radeon lineup, and since 2014, the company has shifted its focus toward gaming, data centers, automotive, and visualization. Founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem, the name “NVIDIA” is derived from the early “NV” abbreviation (next version / next vision) combined with the Latin word “invidia” (envy).

The company has become a major force in the field of artificial intelligence, enabling developers to perform far faster computations by utilizing GPU acceleration via the CUDA platform. As a result, NVIDIA has become an indispensable player in enterprise computing. Today, many organizations rely on NVIDIA GPUs to speed up complex data processing workloads. Additionally, NVIDIA’s strategic investments in the automotive sector have gained significant visibility in recent years. The NVIDIA DRIVE platform, designed for autonomous vehicles, enhances both safety and intelligence on the road. With its wide product portfolio, the NVIDIA brand now represents far more than just a graphics card manufacturer.

GPU, short for “Graphics Processing Unit,” is a specialized processor responsible for performing all graphics-related operations in a computer. While the CPU handles general-purpose tasks, the GPU excels at performing thousands of operations simultaneously at high speed. During gameplay, all the visuals on screen—3D models of characters, buildings, and vehicles, visual effects like explosions, fog, and rain, frame-by-frame animation changes, ray tracing calculations, and much more—are processed by the GPU. Today, GPUs are not only used for graphics rendering but also for processing large datasets. Scientific simulations, machine learning, and all compute-intensive applications require GPU acceleration. In summary, thanks to parallel computing capabilities, GPUs handle complex graphics workloads much faster than CPUs, making them an essential component of modern computing systems.

We know that GPUs play a key role in high-performance gaming, professional visual workflows, AI training, and data science. Cloud-based NVIDIA GPU servers provide these capabilities in a scalable way, enabling faster project execution. The most widely used NVIDIA GPU product families can be categorized as follows:

On that note, you may also be interested in our article titled “What is an LLM? Methods for Maximizing Efficiency with Cloud-Based Models,” where we explain large language models, their types, what to consider when selecting an LLM, and how these technologies relate to one another.

• GeForce Series: The most well-known consumer GPU family focused on gaming performance. RTX models deliver more realistic visuals with ray tracing.
• NVIDIA RTX (formerly Quadro) Professional Series: Optimized for professional workflows such as 3D modeling, CAD, animation, and video editing.
• NVIDIA A Series (A100, A30, A40, etc.): Designed for artificial intelligence training, data science, high-performance computing (HPC), and large-scale data centers.
• H100 / Hopper Architecture: Advanced data center GPUs used in large language models (LLMs) and deep learning applications.
• Tesla Series: NVIDIA’s former professional GPU lineup prior to the transition to RTX and Data Center GPU branding. Today, it has been replaced by the A and H Series.
• NVIDIA DRIVE: A hardware and software platform designed for autonomous driving technologies. It processes sensor data to enhance environmental perception.

Thanks to their advanced parallel processing capabilities, NVIDIA GPU drivers provide significant performance benefits for both creative production and enterprise data processing. Therefore, they are widely preferred by both individual users and organizations requiring high-performance compute resources in the cloud. Common usage areas include:

• Gaming & Graphics Processing: Realistic shading, ray tracing, high FPS, and advanced animation computing
• Artificial Intelligence & Machine Learning: Model training, inference, large language models, and deep learning applications
• 3D Modeling: Architecture, animation, film production, and visualization workflows
• Data Science & Analytics: Rapid processing of large datasets, simulations, and high-performance computing (HPC)
• Video Editing: 4K/8K video processing, timeline acceleration, and real-time effects
• Cloud GPU Servers: Scalable compute resources and remote access for high-performance workloads
• Autonomous Driving Systems: Real-time interpretation of sensor data and autonomous decision-making

As mentioned earlier, NVIDIA GPUs are built on a parallel processing architecture capable of handling large volumes of data simultaneously. This structure provides exceptional speed, especially in graphics rendering, AI training, and large-scale analytics. A GPU renders scenes frame by frame by calculating lighting, shadows, textures, and motion in real time. In AI applications, CUDA and Tensor Cores accelerate complex matrix operations, enabling deep learning models to be trained much faster. Simply put, an NVIDIA GPU is an advanced hardware component that maximizes performance for both graphics and compute workloads by utilizing parallel processing power. Below are the key steps of how NVIDIA GPUs operate:

• A request for data or image processing is sent to the GPU.
• The GPU processes the data in parallel using thousands of cores.
• For graphics tasks, the positions, textures, and lighting of objects in the scene are calculated.
• Complex effects, shadows, and reflections are generated frame by frame.
• For AI workloads, CUDA Cores provide parallel compute capacity, while Tensor Cores accelerate deep learning operations.
• Once processing is complete, the output is delivered to the display or the relevant application.
• The GPU prepares for the next task and the cycle continues.

If you are unsure which NVIDIA GPU model best suits your needs, this section is for you. Selecting an NVIDIA GPU depends on your intended use and the required compute performance. The guide below provides recommended GPU types based on different scenarios:

• Gaming & Entertainment: GeForce RTX Series for high FPS and realistic graphics. Ray tracing-enabled models deliver more immersive visuals.
• 3D Modeling & Animation: RTX Professional or Studio Series for CAD, animation, and rendering workflows.
• Artificial Intelligence & Machine Learning: NVIDIA A Series (A100, A40, etc.) or H100 GPUs optimized for large datasets and deep learning models.
• Video Editing & Production: RTX Studio or A Series GPUs for high-resolution video processing and effects.
• Autonomous Vehicles & Sensor Processing: NVIDIA DRIVE ensures safe and intelligent driving through real-time data processing.
• Cloud & Data Center Applications: Cloud GPU servers provide scalable compute power and accelerate large-scale analytics.

Both NVIDIA and AMD dominate the GPU market with solutions built for gaming, 3D rendering, professional design, and AI applications. However, each brand emphasizes different technologies and strengths. NVIDIA stands out for its parallel compute efficiency and AI-accelerated capabilities, while AMD is recognized for competitive pricing and open-source compatibility advantages. The table below shows a clear AMD vs NVIDIA GPU comparison:

When deployed in cloud environments, NVIDIA GPUs provide major benefits for both enterprises and individual users. Cloud-based GPU solutions particularly simplify workloads that require high computational performance and extensive data processing. For example:

• Eliminates costs related to purchasing and maintaining physical GPU hardware.
• GPU capacity can be increased or reduced based on demand.
• Accelerates workloads such as big data analytics, AI model training, and 3D rendering.
• Provides access to GPU resources from anywhere with an internet connection.
• New GPU resources can be provisioned instantly with zero wait time.
• Reduces physical server footprint and power consumption.