30-Second Explanation
NVIDIA began as a company focused on 3D computer graphics. Today, it is much broader.
It designs GPUs, CPUs, networking technology, complete AI systems, and a large software stack. Its technology can sit underneath cloud AI services, research computing, gaming PCs, industrial simulation, robotics, and autonomous-vehicle development.
The easiest way to understand modern NVIDIA is not as a company that simply "sells GPUs." It increasingly designs the whole computing platform around accelerated computing: processors, high-speed interconnects, networking, systems, software, and developer tools.
NVIDIA does not manufacture most of its advanced chips in factories it owns. It uses outside foundries and manufacturing partners, while concentrating much of its own work on architecture, chip and system design, software, platform engineering, sales, and developer ecosystems. [S1]
How NVIDIA Actually Works
A simplified view of NVIDIA looks like this:
System integrators · AI model makers · partners
Neoclouds · researchers · developers
Robotics · simulation · professional graphics · vehicles
This matters because the company participates in several layers without doing every physical step itself.
NVIDIA can design an advanced processor, rely on another company to fabricate the semiconductor wafer, use memory supplied by another manufacturer, work with contract manufacturers on assembly and testing, and then sell the resulting platform through cloud companies, computer makers, server companies, distributors, or directly to large customers. [S1]
For an ordinary end user, NVIDIA may be almost invisible. A person can use an AI service running on a cloud platform that bought NVIDIA infrastructure without ever buying an NVIDIA-branded product.
What NVIDIA Does
1.Accelerated computing
NVIDIA designs processors optimized for workloads that benefit from large amounts of parallel computation.
That includes:
- artificial intelligence;
- scientific computing;
- data processing;
- simulation;
- computer graphics;
- rendering;
- some robotics and vehicle workloads.
The company's GPUs remain the best-known part of this business, but the platform now also includes CPUs, DPUs, networking and software. [S1]
2.Data-center systems
Modern AI workloads often require many processors working together.
NVIDIA therefore designs systems and rack-scale platforms, not only individual chips.
Products in the DGX family illustrate this shift from "component supplier" toward "complete computing system."
3.Networking
When thousands of processors work on one AI problem, moving data between them becomes part of the computing problem.
NVIDIA develops NVLink and high-performance Ethernet and InfiniBand networking. Its networking position expanded substantially after the acquisition of Mellanox Technologies. [S1][S5]
4.Software
NVIDIA's hardware is tied to a large software ecosystem.
The most important example is CUDA, which allows developers to use NVIDIA GPUs for general-purpose parallel computing rather than graphics alone.
NVIDIA also develops libraries, SDKs, AI software, enterprise software, models, development tools, and domain-specific frameworks. [S1][S4]
5.Graphics and gaming
NVIDIA still has a major consumer graphics business.
GeForce serves gamers, creators, and increasingly users who run AI workloads locally on PCs.
6.Automotive and physical AI
NVIDIA DRIVE provides hardware and software used in advanced driver-assistance and autonomous-driving development.
NVIDIA also positions simulation, robotics and digital twins as part of what it calls physical AI. [S1][S12]
Products, Platforms & Services
CUDA
CUDA is NVIDIA's parallel computing platform and programming model.
Why it exists: to let developers use GPU parallel-processing capabilities for workloads beyond traditional graphics.
CUDA is one of the reasons NVIDIA became a computing-platform company rather than remaining only a graphics-hardware company.
GeForce
GeForce is NVIDIA's consumer GPU family.
Why it exists: to serve PC gaming, creators, visualization and increasingly local AI applications.
GeForce is also an important reminder that NVIDIA did not abandon its graphics business when data-center AI became much larger.
Blackwell
Blackwell is a computing architecture and broader data-center platform generation.
Why it exists: large AI models require processors, memory, interconnects, networking, system design and software to work together at much larger scale.
Rubin
DGX
DGX is NVIDIA's family of integrated AI computing systems and infrastructure.
Why it exists: customers building AI often need a complete, validated system rather than a loose collection of GPUs.
Omniverse
Omniverse is a collection of technologies for simulation, digital twins and physical-AI workflows.
Why it exists: robots, factories and other physical systems often need to be modeled and tested in simulation before deployment.
NVIDIA DRIVE
NVIDIA DRIVE is NVIDIA's automotive computing and software platform.
Why it exists: advanced driving systems require high-performance in-vehicle AI compute, sensor processing, simulation, training and safety-oriented software.
The NVIDIA Stack
One reason NVIDIA is easy to misunderstand is that its products live at different layers.
NVIDIA controls or designs much of the upper and middle stack.
It does not own every physical manufacturing layer at the bottom.
That combination — broad system control without owning leading-edge semiconductor fabs — is central to how the company operates.
Who Uses NVIDIA Technology?
NVIDIA's users are broader than its direct customers.
Cloud and AI infrastructure operators
Major public and private cloud providers use NVIDIA computing and networking platforms to deliver AI and accelerated computing to their own customers. [S1]
AI model makers
AI companies can train and run models on NVIDIA infrastructure either by purchasing systems directly or by accessing NVIDIA-powered cloud capacity.
Enterprises
Companies use NVIDIA systems and software for AI applications, simulation, data processing, fraud detection, customer support, engineering and other workloads. [S1]
Researchers
Scientists use GPU computing for applications ranging from molecular simulation to climate forecasting and genomics. [S1]
Gamers and creators
Automakers and robotics developers
Automotive and robotics companies use NVIDIA hardware, software and simulation tools to train, test and deploy AI systems that operate in the physical world. [S12]
Who Pays NVIDIA?
The people who use NVIDIA-powered technology are often not the companies that pay NVIDIA directly.
NVIDIA's FY2026 filing distinguishes direct customers from indirect customers. [S1]
Direct customer types
Examples include:
- add-in board manufacturers;
- distributors;
- ODMs;
- OEMs;
- cloud service providers;
- AI model makers;
- system integrators.
These organizations buy from NVIDIA directly.
Indirect customer types
Examples include:
- cloud service providers purchasing through other channels;
- neocloud builders;
- AI model makers;
- enterprises;
- public-sector entities.
These customers may buy NVIDIA-powered infrastructure through distributors, system integrators or other direct customers.
Why this matters
The money path can look like this:
Enterprise / AI developer
↓ pays for cloud capacity
Cloud provider
↓ buys infrastructure
NVIDIAOr:
PC gamer
↓ buys finished graphics card or PC
Board maker / PC manufacturer / retailer
↓ participates in channel
NVIDIAThis is why "Who uses NVIDIA?" and "Who pays NVIDIA?" are not the same question.
Customer concentration
NVIDIA disclosed that in fiscal 2026, one direct customer represented 22% of total revenue and another represented 14%. The filing does not name those two direct customers in that disclosure. [S1]
The important point is not to guess their identities. A relatively small number of very large customers can account for a meaningful share of NVIDIA's business.
How NVIDIA Makes Money
NVIDIA makes money by selling a combination of:
- processors;
- data-center systems;
- networking products;
- graphics products;
- automotive platforms;
- software and licenses;
- services.
Its FY2026 reporting has two reportable segments:
Compute & Networking
GraphicsCompute & Networking includes data-center accelerated computing, networking, AI solutions and software, and automotive platforms.
Graphics includes GeForce gaming GPUs and professional workstation graphics. [S1]
The useful way to understand the model is:
Design high-value compute architecture
↓
Build a hardware + software platform
↓
Use external manufacturing partners
↓
Sell through direct customers and ecosystem partners
↓
Make the platform useful through CUDA and software
↓
Customers move to newer platform generations over timeThe business therefore depends on more than individual chip performance.
NVIDIA competes on system performance, software, networking, developer support, distribution, manufacturing availability and total system cost. [S1]
What NVIDIA Depends On
NVIDIA is powerful in design and software, but it is not self-sufficient.
Semiconductor foundries
Memory suppliers
NVIDIA says it purchases memory from:
- SK hynix;
- Micron;
- Samsung. [S1]
Memory is especially important in AI systems because large models need high-bandwidth access to large amounts of data.
Advanced packaging
NVIDIA says it uses CoWoS packaging technology. [S1]
Advanced packaging matters because modern AI processors increasingly combine large compute dies, high-bandwidth memory and high-speed interconnects.
Assembly, testing and manufacturing partners
NVIDIA's filing names partners including:
- Hon Hai Precision Industry / Foxconn;
- Wistron;
- Fabrinet. [S1]
These companies perform parts of assembly, testing and packaging.
Cloud and system ecosystem
NVIDIA also depends on cloud providers, OEMs, ODMs, system integrators and software developers to deploy its technology at scale. [S1]
Who Depends on NVIDIA?
"Depends on" should not be interpreted as "cannot function without NVIDIA."
The useful question is where NVIDIA has become an important infrastructure provider.
AI model developers
Many model developers use NVIDIA GPUs, networking and CUDA-based software directly or through cloud providers.
Cloud and neocloud operators
Cloud providers offer NVIDIA-powered computing because customers demand GPU-accelerated AI and HPC capacity.
At the same time, some large cloud companies design their own AI accelerators, making the relationship both cooperative and competitive.
Server and system manufacturers
OEMs and ODMs build systems around NVIDIA platforms and reference designs.
Developers and researchers
A large body of software is optimized for CUDA and NVIDIA GPUs.
That creates technical continuity across hardware generations.
Gaming ecosystem
Game developers and PC manufacturers target GeForce and RTX capabilities because the installed user base makes those features commercially relevant.
Company Relationship Map
The goal of this map is to show what the relationship is, not simply list related names.
Upstream manufacturing
| Entity | Relationship | Scope |
|---|---|---|
| TSMC | Foundry / manufacturing partner | Semiconductor wafer fabrication |
| Samsung | Foundry + memory supplier | Wafers and memory |
| SK hynix | Memory supplier | High-performance memory |
| Micron | Memory supplier | Memory |
| Foxconn / Hon Hai | Contract manufacturing partner | Assembly / manufacturing |
| Wistron | Contract manufacturing partner | Assembly / manufacturing |
| Fabrinet | Contract manufacturing partner | Assembly / testing / packaging |
Source: NVIDIA FY2026 Form 10-K. [S1]
Corporate history
| Entity | Relationship | Scope |
|---|---|---|
| Mellanox Technologies | Acquired by NVIDIA | High-performance networking |
| Jensen Huang | Co-founder, President and CEO | Leadership |
Competition and ecosystem overlap
NVIDIA's 10-K identifies several companies that compete in specific layers. [S1]
| Entity | Relationship | Scope |
|---|---|---|
| AMD | Competitor | GPUs and accelerated computing; also other compute/networking areas |
| Intel | Competitor | GPUs, CPUs and accelerated computing |
| Huawei | Competitor | Accelerated computing and other platform layers |
| Alphabet | Cloud ecosystem + competitor | Internal AI computing hardware |
| Amazon | Cloud ecosystem + competitor | Internal AI hardware and Arm CPUs |
| Microsoft | Cloud ecosystem + competitor | Internal AI hardware and Arm CPUs |
| Broadcom | Competitor | SoCs and networking |
| Arista Networks | Competitor | Networking |
| Cisco | Competitor | Networking |
| Qualcomm | Competitor | Embedded / automotive SoCs |
| Tesla | Competitor through internal design | Automotive / embedded AI compute |
Important nuance:
A large technology company can be an NVIDIA customer or ecosystem partner in one context and a competitor in another.
That is more accurate than forcing every relationship into one label.
Competition by Business Layer
Accelerated computing and GPUs
Examples identified by NVIDIA:
- AMD
- Intel
- Huawei
Competition involves hardware performance, software support, price, energy efficiency and platform availability. [S1]
Custom AI processors
Large cloud companies including Alphabet, Amazon and Microsoft design internal accelerators or other computing hardware.
They may still deploy NVIDIA systems while also trying to reduce dependence on third-party accelerators for some workloads.
CPU and data-center compute
NVIDIA's Grace CPUs compete in a market that includes Arm-based and internally designed processors from other large technology companies.
Networking
NVIDIA identifies competitors including:
- AMD;
- Arista Networks;
- Broadcom;
- Cisco;
- HPE;
- Huawei;
- Intel;
- Lumentum;
- Marvell. [S1]
Automotive and embedded computing
Competitors include companies such as:
- Qualcomm;
- Intel;
- AMD;
- Samsung;
- Renesas;
- Tesla through internal chip design. [S1]
The useful point is that NVIDIA does not compete in one market. It competes at multiple layers of the computing stack.
History
1993 — NVIDIA is founded
NVIDIA was founded on April 5, 1993, by Jensen Huang, Chris Malachowsky and Curtis Priem, with an early focus on 3D graphics. [S2]
1999 — GPU and GeForce
NVIDIA's graphics business helped popularize the GPU as a distinct computing category. GeForce became a major consumer product family. [S2]
2006 — CUDA
2012 — Deep learning breakthrough
NVIDIA GPUs helped power AlexNet, a major milestone in deep learning and image recognition. [S2]
2016 — DGX-1
NVIDIA introduced DGX-1, an integrated deep-learning system that combined GPUs, software and development tools. [S9]
2018 — RTX
NVIDIA introduced RTX, extending real-time ray tracing and AI-assisted graphics. [S2]
2020 — Mellanox acquisition
NVIDIA completed its $7 billion acquisition of Mellanox Technologies, adding high-performance networking capability. [S5]
2024 — Blackwell
How NVIDIA Changed
| Dimension | Earlier NVIDIA | NVIDIA today |
|---|---|---|
| Core identity | PC graphics company | Accelerated-computing and AI infrastructure company |
| Main scale | Individual PC component | Data-center and rack-scale systems plus PC graphics |
| Software role | Supports graphics hardware | Central developer and AI platform |
| Networking | Not a major core capability | Integral to large AI systems |
| Customer mix | Gaming and PC ecosystem | Cloud, AI, enterprise, research, automotive and gaming |
| Product scope | Graphics processors | Compute + networking + systems + software |
| Manufacturing | Fabless model | Still fabless, but with far larger supply-chain coordination |
| Developer ecosystem | Graphics developers | AI, HPC, science, enterprise, robotics, graphics and more |
The biggest structural change is not simply that "AI became popular."
NVIDIA gradually expanded up and down the stack:
GPU
↓
Programmable GPU
↓
CUDA ecosystem
↓
AI software
↓
Integrated systems
↓
High-performance networking
↓
Rack-scale AI infrastructureTurning Points
Turning Point 1 — Treating the GPU as a programmable processor
Graphics created the hardware foundation, but programmability made the GPU useful for more than drawing images.
Turning Point 2 — CUDA
CUDA reduced the gap between specialized graphics hardware and general-purpose software development.
It created a developer layer that could survive across many generations of NVIDIA hardware.
Turning Point 3 — Deep learning adopts GPU computing
The 2012 AlexNet milestone showed how effective GPUs could be for modern neural networks. [S2]
NVIDIA did not invent neural networks, but its hardware and software became well matched to the computational needs of deep learning.
Turning Point 4 — DGX turns components into systems
DGX-1 showed that NVIDIA was willing to sell a purpose-built AI system, not only processors. [S9]
Turning Point 5 — Mellanox makes networking strategic
The Mellanox acquisition added high-performance networking at exactly the time AI workloads were scaling across large clusters. [S5]
Turning Point 6 — Blackwell and Rubin make the data center the unit of design
NVIDIA increasingly describes and designs the entire data center as one computing system, where GPU, CPU, NVLink, Ethernet/InfiniBand, memory, software and cooling must work together. [S1][S6][S7]
For the full narrative, read How NVIDIA Became an AI Company.
Leadership
Jensen Huang
Jensen Huang is NVIDIA's co-founder, president and CEO.
He has led the company since its founding and is unusually central to its long-term technical and strategic direction. [S3]
Chris Malachowsky
Chris Malachowsky is a co-founder and NVIDIA Fellow. [S3]
Curtis Priem
Curtis Priem was NVIDIA's third co-founder and was involved in the company's early technology development. [S2]
Major Acquisition: Mellanox Technologies
NVIDIA completed the acquisition of Mellanox Technologies on April 27, 2020, for a transaction value of $7 billion. [S5]
What Mellanox added
Mellanox specialized in high-performance data-center networking and interconnects.
Why it mattered
Large AI systems are limited not only by how fast one GPU can calculate, but by how efficiently many processors can exchange data.
The acquisition therefore helped NVIDIA move from:
fast processorstoward:
compute + networking + complete data-center architectureThat capability is visible in NVIDIA's current Blackwell and Rubin system designs.
What Most People Miss
1. NVIDIA is a chip designer, not primarily a chip-factory owner
NVIDIA's own filing describes a fabless and contract-manufacturing strategy.
It relies on partners for wafer fabrication, assembly, testing and packaging. [S1]
This means "NVIDIA makes GPUs" is commercially understandable but physically incomplete.
NVIDIA designs the processors and platforms; other companies manufacture many of the physical components.
2. Networking is now part of the computing product
3. NVIDIA's cloud partners can also be competitors
Alphabet, Amazon and Microsoft participate in cloud ecosystems where NVIDIA hardware is deployed, but NVIDIA also identifies large cloud companies with internal AI or accelerated-computing hardware as competitors. [S1]
The relationship is not simply customer versus competitor.
It can be both.
4. CUDA may outlast any single GPU generation
Blackwell will eventually be replaced by Rubin, and Rubin will eventually be replaced by another architecture.
CUDA provides software continuity across hardware generations.
That makes the software ecosystem strategically different from an individual chip generation.
5. The company increasingly sells systems, not just chips
NVIDIA's own FY2026 filing describes data-center infrastructure delivered as rack-scale systems, subsystems or modules, along with software and services. [S1]
This is a major reason the simple label "GPU company" is now incomplete.
Common Misconceptions
"NVIDIA manufactures its own advanced chips."
Not exactly.
NVIDIA designs the chips and systems but says it uses outside suppliers for wafer fabrication, assembly, testing and packaging. TSMC and Samsung are among the foundries named in its FY2026 filing. [S1]
"NVIDIA recently pivoted from gaming to AI."
"NVIDIA only sells GPUs."
No.
Its current platform includes CPUs, DPUs, networking, systems, software, services and automotive technology in addition to GPUs. [S1]
"All of NVIDIA's cloud customers are just customers."
Not necessarily.
Some large cloud companies buy or deploy NVIDIA technology while also developing their own accelerators or CPUs and competing in parts of the stack. [S1]
"Mellanox was a random acquisition."
No.
Mellanox added high-performance networking, which became increasingly important as AI systems scaled from single servers to very large clusters. [S5]
Common Questions
Is NVIDIA an American company?
Yes. NVIDIA is headquartered in Santa Clara, California, United States. [S1]
Who founded NVIDIA?
Jensen Huang, Chris Malachowsky and Curtis Priem founded NVIDIA in 1993. [S2]
Who owns NVIDIA?
NVIDIA is a publicly traded company listed on NASDAQ under the ticker NVDA. It does not have a corporate parent.
Does NVIDIA make its own chips?
NVIDIA designs its processors but uses outside foundries and manufacturing partners to fabricate and assemble many of the physical products. [S1]
What does NVIDIA sell?
NVIDIA sells processors, graphics products, networking, complete computing systems, software, automotive platforms and related services.
Is CUDA a product or a programming language?
CUDA is a parallel computing platform and programming model. It is not simply a programming language. [S4]
Is GeForce still important to NVIDIA?
Yes. GeForce remains NVIDIA's core consumer graphics family, even though data-center computing is now much larger in strategic scope.
What is NVIDIA's CIK?
NVIDIA Corporation's SEC Central Index Key is 0001045810.
What Changed Recently
May 2026 — Vera Rubin moved into full-production ramp
NVIDIA said its Vera Rubin platform was ramping into full production, marking the transition to the generation after Blackwell. [S8]
Why it matters: NVIDIA's platform cadence is becoming faster, and the product is increasingly a multi-chip, full-system architecture rather than a standalone GPU.
2026 — CUDA reached its 20-year milestone
NVIDIA marked 20 years of CUDA at GTC 2026. [S14]
Why it matters: CUDA is no longer a side technology attached to graphics. It is a long-lived software layer spanning many hardware generations.
Fiscal 2026 — Blackwell shifted the mix toward full-scale data-center solutions
NVIDIA's FY2026 filing says its business model transitioned further from Hopper HGX systems toward Blackwell full-scale data-center solutions. [S1]
Why it matters: NVIDIA is taking responsibility for more of the infrastructure stack.
Fiscal 2026 — Customer concentration remained significant
NVIDIA disclosed two direct customers representing 22% and 14% of fiscal-year revenue. [S1]
Why it matters: the company serves a broad end-user ecosystem, but a small number of very large direct buyers can still represent a significant part of sales.
Evidence
Claim: NVIDIA uses a fabless manufacturing strategy
Plain-English meaning: NVIDIA designs the technology but relies on outside suppliers for fabrication, assembly, testing and packaging.
Primary source: NVIDIA FY2026 Form 10-K, Manufacturing section. [S1]
Claim: TSMC and Samsung are named foundries
Primary source: NVIDIA FY2026 Form 10-K. [S1]
Claim: NVIDIA purchases memory from SK hynix, Micron and Samsung
Primary source: NVIDIA FY2026 Form 10-K. [S1]
Claim: Mellanox was acquired for $7 billion
Primary source: NVIDIA acquisition completion announcement, April 27, 2020. [S5]
Claim: NVIDIA reports Compute & Networking and Graphics as its two reportable segments
Primary source: NVIDIA FY2026 Form 10-K. [S1]
Claim: Rubin entered full-production ramp in 2026
Primary source: NVIDIA announcement, May 31, 2026. [S8]
Explore NVIDIA
Products & Platforms
- CUDA
- GeForce
- Blackwell
- Rubin
- DGX
- Omniverse
- NVIDIA DRIVE
People
- Jensen Huang
Related Companies
- Mellanox Technologies
Industry
Story
- How NVIDIA Became an AI Company