NVIDIA

NVIDIA Corporation
Type	Public
Traded as	NASDAQ: NVDA NASDAQ-100 component S&P 100 component S&P 500 component
Industry	Semiconductors, Artificial Intelligence, Computer graphics
Founded	April 5, 1993
Founders	Jensen Huang Chris Malachowsky Curtis Priem
Headquarters	Santa Clara, California, U.S.
Key people	Jensen Huang (President & CEO) Colette Kress (EVP & CFO)
Products	Graphics Processing Units (GPUs) AI accelerators Data center solutions CUDA Deep learning platforms
Revenue	$130.5 billion (FY 2025)
Operating income	$81.5 billion (FY 2025)
Net income	$72.9 billion (FY 2025)
Market cap	$4.5+ trillion (September 2025)
Employees	36,000+ (2025)
Website	[https://[ ] [ ]]

NVIDIA Corporation is an American multinational technology company that has emerged as the dominant force in artificial intelligence computing infrastructure. Founded in 1993, NVIDIA initially focused on graphics processing units (GPUs) for gaming but has since transformed into the world's most valuable semiconductor company and a cornerstone of the global AI revolution. The company's GPUs and associated software platforms have become essential for training and deploying deep learning models, powering everything from large language models like ChatGPT to autonomous vehicles and scientific computing applications. As of 2025, NVIDIA controls approximately 80% of the market for GPUs used in AI model training and deployment, with its chips powering over 75% of the world's TOP500 supercomputers.

History

Founding and Early Years

NVIDIA was founded on April 5, 1993, by Jensen Huang, Chris Malachowsky, and Curtis Priem at a Denny's restaurant in East San Jose, California. The three engineers, who had previously worked at companies including LSI Logic, Sun Microsystems, and IBM, started the company with $40,000 in initial capital. The company name derives from the Latin word "invidia," meaning envy, as the founders wanted competitors to be "green with envy."

The company initially struggled, coming close to bankruptcy in 1996 when their first GPU product, the NV1, failed due to its use of quadrilateral primitives rather than the triangle primitives that Microsoft's DirectX would standardize. Nvidia survived by securing a contract buyout from Sega for $5 million, which gave the company what Huang later described as "six months to live." This funding allowed Nvidia to develop the RIVA 128 in 1997, which sold about a million units within four months and saved the company.

GPU Revolution

In 1999, NVIDIA introduced the GeForce 256, marketed as the world's first Graphics Processing Unit (GPU), which featured hardware transform and lighting (T&L) capabilities that offloaded graphics processing from the CPU. This innovation established Nvidia's dominance in the gaming graphics market and laid the foundation for future developments in parallel computing. The company went public on January 22, 1999.

Pivotal Role in AI Revolution

CUDA Platform Development

The turning point for NVIDIA's AI journey came with the introduction of CUDA (Compute Unified Device Architecture) in 2006-2007, led by Ian Buck who joined NVIDIA after developing the Brook programming model at Stanford. CUDA enabled developers to use GPUs for general-purpose computing on graphics processing units (GPGPU), allowing the massive parallel processing power of GPUs to be applied to non-graphics applications. NVIDIA invested over a billion dollars in developing CUDA at a time when AI applications were not yet fully apparent, demonstrating significant foresight.

Deep Learning Breakthrough

In 2009, NVIDIA became involved in what was called the "big bang" of deep learning when Google Brain team members, led by Andrew Ng, discovered that GPUs could dramatically accelerate neural network training, speeding up machine learning by approximately 100 times. The watershed moment came in 2012 when AlexNet, a convolutional neural network trained using two Nvidia GeForce GTX 580 GPUs, won the ImageNet Large Scale Visual Recognition Challenge by a massive margin. This event demonstrated conclusively the power of deep learning on GPUs and triggered a massive shift in the AI research community towards using Nvidia's hardware.

By 2015, major technology companies including Google, Microsoft, and Baidu had achieved superhuman performance in tasks like image recognition and speech understanding using deep neural networks running on Nvidia GPUs.

AI Hardware Products

GPU Architectures

Since the deep learning boom, Nvidia has designed its GPU architectures with AI-specific features. A key innovation was the introduction of the Tensor Core, a specialized processing unit designed to accelerate the matrix multiply-accumulate operations fundamental to training and running neural networks.

Key Nvidia GPU Microarchitectures for AI

Architecture	Year	Key AI Features	Notable GPU(s)	AI Performance
Volta	2017	First-generation Tensor Cores, mixed precision	Tesla V100	125 TFLOPS (Tensor)
Turing	2018	Second-generation Tensor Cores with INT8/INT4	Quadro RTX 8000, Titan RTX	130 TFLOPS (Tensor)
Ampere	2020	Third-generation Tensor Cores with TF32 and Sparsity	A100	312 TFLOPS (TF32)
Hopper	2022	Fourth-generation Tensor Cores, Transformer Engine	H100, H200	1 PFLOPS (FP8)
Blackwell	2024	Fifth-generation Tensor Cores, FP4 precision, dual-die GPU	B200, GB200	20 PFLOPS (FP4)

Hopper Architecture

The H100 GPU, based on the Hopper architecture announced in 2022, delivered significant performance improvements with features including:

• Fourth-generation Tensor Cores with FP8 precision support
• Up to 80GB of HBM3 memory with 3TB/s bandwidth
• Transformer Engine optimized for large language models
• Up to 60 teraflops of FP64 computing for high-performance computing

Blackwell Architecture

Announced in March 2024, the Blackwell architecture represents NVIDIA's latest generation:

• B200 GPU: Features 208 billion transistors across two reticle-limited dies connected via 10 TB/s chip-to-chip interconnect
• GB200 Superchip: Combines Grace CPU with two Blackwell GPUs
• GB200 NVL72: Integrates 36 Grace-Blackwell superchips (72 GPUs + 36 Grace CPUs) as a single 72-GPU NVLink domain for real-time trillion-parameter inference
• 192GB of HBM3e memory with 8TB/s bandwidth per GPU
• Second-generation Transformer Engine with FP4 support
• Up to 25x less cost and energy consumption compared to previous generation for LLM inference

DGX Systems

NVIDIA's DGX line represents complete AI computing platforms:

NVIDIA DGX System Evolution

Model	Year	GPUs	Memory	AI Performance	Key Features
DGX-1	2016	8x Tesla P100/V100	128-256GB	1 PFLOPS	First AI supercomputer "in a box"
DGX-2	2018	16x Tesla V100	512GB	2 PFLOPS	NVSwitch interconnect
DGX A100	2020	8x A100	320-640GB	5 PFLOPS	Universal AI system
DGX H100	2022	8x H100	640GB	32 PFLOPS (FP8)	Transformer Engine
DGX B200	2024	8x B200	1,536GB	144 PFLOPS	FP4 precision
DGX GB200	2024	8x GB200	1,440GB HBM3e + 768GB LPDDR5X	144 PFLOPS	Grace-Blackwell Superchips
DGX Spark	2025	1x GB10 Superchip	128GB unified LPDDR5x	1 PFLOPS (FP4)	Desktop AI supercomputer, $3,999

Notably, NVIDIA donated the first DGX-1 to OpenAI in 2016, which was later used to train models leading to ChatGPT.

Networking and Interconnects

• NVLink: Fifth-generation NVLink enables up to 1.8 TB/s per Blackwell GPU, with NVLink Switch forming large GPU fabrics
• Mellanox InfiniBand: Following the $7 billion acquisition in 2020, provides high-speed networking crucial for AI clusters
• Quantum-2 InfiniBand: Delivers ultra-low latency for scale-out training clusters

Edge AI and Robotics

• NVIDIA Jetson: Embedded modules and development kits for edge AI and robotics (for example Jetson Orin)
• NVIDIA Drive: Full-stack platform for autonomous vehicles, including Drive Thor (2025)

AI Software Stack

CUDA Ecosystem

The CUDA ecosystem, first released in 2006, has created what analysts describe as an "impenetrable moat" for Nvidia:

Core CUDA Software Components

Component	Purpose	AI Application
CUDA Toolkit	Parallel computing platform and APIs	Foundation for all GPU computing
cuDNN	GPU-accelerated DNN primitives	Convolutions, attention, matmul, normalization
TensorRT	Inference optimization SDK	Model optimization, quantization, deployment
Triton Inference Server	Multi-framework model serving	Production deployment, dynamic batching
RAPIDS	GPU-accelerated data science	End-to-end ML pipelines on GPU
NeMo	Generative AI framework	LLMs, multimodal, speech models
NIM	Inference microservices	Production-ready AI endpoints

NVIDIA AI Enterprise

NVIDIA AI Enterprise is a cloud-native suite providing:

• Curated and validated software stack
• Extended-life software branches for API stability
• Enterprise-grade support
• Integration with major cloud providers

Framework Integration

Nvidia ensures deep integration with all major deep learning frameworks:

• TensorFlow: Extensive CUDA kernel optimization since 2015
• PyTorch: Native CUDA support with optimized operations
• JAX, MXNet, and other frameworks

Market Position and Financial Performance

Revenue Growth

Nvidia's financial performance has seen explosive growth driven by AI demand:

• Fiscal Year 2025: $130.5 billion revenue (114% year-over-year growth)
• Data Center revenue: Reached $35.6 billion in Q4 FY2025 alone (93% YoY growth)
• Q2 FY2026: $46.7 billion quarterly revenue

Market Capitalization Milestones

Nvidia Market Capitalization Growth

Date	Market Cap	Milestone
June 2023	$1 trillion	First semiconductor company to reach $1T
February 2024	$2 trillion	Doubled in 9 months
June 2024	$3 trillion	Briefly world's most valuable company
July 2025	$4 trillion	First company to reach $4T
September 2025	$4.5+ trillion	Current valuation

Market Dominance

• Controls approximately 80% of AI GPU market share
• Provides chips for over 75% of TOP500 supercomputers
• Used to train over 95% of AI models in data centers
• 91% of AI research papers in 2024 used Nvidia hardware

Strategic Partnerships and Acquisitions

Key Acquisitions

Major Nvidia Acquisitions for AI

Year	Company	Focus	Amount	Impact
2019	Mellanox Technologies	High-performance networking	$6.9 billion	Enabled complete data center solutions
2020	Cumulus Networks	Network software	Undisclosed	Enhanced software-defined networking
2022	Failed Arm Limited bid	CPU architecture	$40 billion (terminated)	Blocked by regulators
2024	Run:ai	AI workload optimization	$700 million	Enhanced Kubernetes integration
2024	OctoAI	Enterprise generative AI	$250 million	Strengthened AI deployment
2025	CentML	ML services	Up to $400 million	Expanded AI software capabilities

Strategic Partnerships

• OpenAI: September 2025 agreement to deploy 10GW of AI infrastructure using Nvidia hardware, up to $100 billion investment
• Cloud Providers: Deep integration with AWS (P5 instances), Google Cloud (A3/A3 Mega), Microsoft Azure (ND H100 v5)
• Intel: Joint AI infrastructure development announced 2025
• Enterprise Partners: ServiceNow, Salesforce, SAP leverage Nvidia AI Enterprise

Impact on AI Development

Enabling Breakthroughs

Nvidia's technology has been instrumental in:

• Training of GPT-3, GPT-4, and ChatGPT (using 10,000 Nvidia GPUs)
• Development of text-to-image models like DALL-E and Stable Diffusion
• Advancement of autonomous driving (used by Tesla, Waymo, and others)
• Acceleration of drug discovery and protein folding research (AlphaFold)
• Enabling robotics and digital twins through Omniverse platform

MLPerf Benchmarks

Nvidia regularly leads MLPerf benchmark results:

• MLPerf Inference v4.0: Top results for Llama-2-70B and Stable Diffusion XL workloads
• MLPerf Training v4.0: Leading performance across all categories

Competition and Challenges

Competitors

Despite Nvidia's dominance, several companies are attempting to challenge its position:

• AMD: Launched MI300 series GPUs targeting AI workloads
• Intel: Developing Gaudi and other AI accelerators
• Google: Custom Tensor Processing Units (TPUs) for internal use and cloud
• Emerging startups: Developing specialized AI chips

Supply Chain Constraints

• H100 GPUs had lead times of 36-52 weeks during peak demand in 2023
• Production constraints at TSMC for advanced nodes
• HBM memory supply limitations

Geopolitical Challenges

U.S. export restrictions to China have impacted NVIDIA's business:

• Development of China-specific products (H20) to comply with regulations
• Estimated $8 billion revenue impact in 2025

Future Directions

Physical AI and Robotics

Jensen Huang has identified "Physical AI" as the next major wave:

• Embodied AI for robotics applications
• Digital twins and NVIDIA Omniverse platform
• Isaac platform for humanoid robots

AI Factories

NVIDIA has introduced the concept of "AI Factories" - facilities that produce intelligence rather than physical goods, representing a new class of data center specifically designed for generative AI and large language model training.

Quantum Computing

• CUDA Quantum platform for hybrid classical-quantum computing
• Partnerships with quantum hardware providers
• Quantum research center in Japan

Analyst Outlook

• Melius Research: Projects Nvidia could capture 40% of $2 trillion AI infrastructure market by 2030
• Beth Kindig (I/O Fund): Predicts NVIDIA will become $10 trillion company by 2030
• Grand View Research: Estimates AI spending will grow 37% annually through 2030

See Also

• Graphics Processing Unit
• CUDA
• Deep Learning
• Artificial Intelligence Hardware
• Jensen Huang
• Tensor Core
• Large Language Models
• Transformer (machine learning model)
• MLPerf
• Data Center
• High Performance Computing
• Parallel Computing
• Autonomous Vehicles
• Robotics
• Digital Twin