Nvidia’s Revenue Breakdown by End-Market (2017-2022)

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This statistic highlights Nvidia’s Revenue by End-Market, across Data Center, Gaming, OEM & IP, Automotive and Professional Visualization, reported on a quarterly basis from Q1 2017 onwards.
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This statistic highlights Nvidia’s Revenue Breakdown by End-Market, across Data Center, Gaming, OEM & IP, Automotive and Professional Visualization, reported on a quarterly basis from Q1 2017 onwards.

Nvidia’s Revenue Breakdown by End-Market

Nvidia generated $7.64 billion in revenue during Q4 2022 alone, which is further divided into Automotive, Datacenter, Gaming, OEM/IP and Professional Visualization end-markets.

End-Market Revenue in Q4 2022 Contribution in Q4 2022
Auto            125.0 1.60%
Datacenter        3,263.0 42.70%
Gaming        3,420.0 44.70%
OEM & IP            192.0 2.50%
Professional Visualization            643.0 8.40%

(All figures in USD million, except percentages)

Gaming

Gaming segment generated 50.9% of the total revenue .NVIDIA targets the gaming market with GeForce RTX and GeForce GTX GPUs for PC gaming, SHIELD devices for gaming and streaming, GeForce NOW for cloud-based gaming, as well as platforms and development services for specialized console gaming devices.

Computer gaming is an ever-evolving industry with several factors propelling its growth, including new high production value games and franchises, shifting focus towards eSports, the boom in competitive online gaming and the rise of virtual and augmented reality.

To enhance the user experience, these gaming platforms utilize advance 3D software and algorithms which provide special gaming effects. To optimize the PC user’s settings for each title and provide them with an option to share and record gameplay, there is a gaming application provided by the company, GeForce Experience which has garnered an optimistic response with over 100 million user downloads.

Professional Visualization

NVIDIA serves this market segment by partnering with independent software vendors to optimize their offerings for NVIDIA GPUs. Virtual reality is gaining greater importance in the gaming industry and this requires simulating the physical behavior of light and materials, or physically-based rendering, an emerging trend in professional design. 

The company’s software DesignWorks delivers this to designers allowing them to interact with their model in real-time, scale the display capabilities and develop photorealistic renderings for the client.

Datacenter

NVIDIA serves the datacenter market through AI and HPC applications. It also serves the datacenter market with GRID for virtualized graphics. This makes it possible to run graphics-intensive applications remotely on a server in the datacenter.

In the field of AI, NVIDIA’s platform accelerates both deep learning and machine learning workloads. Deep learning is a computer science approach which trains neural networks to recognize patterns from large amounts of data in the form of images, text and sound, in some cases better than humans. Machine learning is a related approach that leverages algorithms as well as data to learn how to make determinations or predictions, often used in data science. HPC, also referred to as scientific computing, uses numerical computational approaches to solve large and complex problems. 

For both AI and HPC applications, the NVIDIA accelerated computing platform greatly increases the performance and power efficiency of high-performance computers and datacenters, as GPUs excel at parallel workloads. For example, an NVIDIA GPU-accelerated machine learning cluster for data science is 1/8 the cost, 1/15 the space, and 1/18 the power of a traditional CPU-based cluster.

Automotive

NVIDIA’s Automotive market is comprised of cockpit infotainment solutions, AV platforms, and associated development agreements. They are delivering a full solution for the AV market under the DRIVE brand. The company has demonstrated multiple applications of AI within the car where it can drive the car itself as a pilot, in either partial or fully autonomous mode and may be a co-pilot, helping the human driver in making a safer driving expertise.

They are working with several hundred partners in the automotive ecosystem including automakers, truck makers, tier-one suppliers, sensor manufacturers, automotive research institutions, HD mapping companies, and startups to develop and deploy AI systems for self-driving vehicles and hence, the driving experience.

The NVIDIA DRIVE computing platform consists of high-performance, energy efficient hardware – DRIVE AGX, and open, modular software – including DRIVE AV for autonomous driving and DRIVE IX for in-vehicle AI assistance. It can perceive and understand in real-time what’s happening around the vehicle, precisely locate itself on an HD map, and plan a safe path forward. This advanced self-driving car platform combines deep learning, sensor fusion, and surround vision to change the driving experience. 

Company Overview

NVIDIA, headquartered in Santa Clara, California, was incorporated in California in April 1993 and later reincorporated in Delaware in April 1998. With the shifting focus on PC graphics, the company invented Graphics Processing Unit (GPU) in 1999 and has also extended its focus towards the revolutionary field of Artificial Intelligence (AI) in the recent years. 

The growing demand for 3D graphics coupled with high scalability of the gaming market is aptly addressed by the company through evolution of the GPU into a computer brain through a combination of virtual reality, high performance computing (HPC) and AI.

The company’s two major segments are GPU and Tegra Processor which are based on a single underlying architecture. From these proprietary processors, the company has strategically designed, created and marketed platforms which cater to four large market segments with high efficiency: Gaming, Professional Visualization, Data Center, and Automotive.

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