NVIDIA: Background and History

NVIDIA was founded in 1993 and is headquartered in Santa Clara, California. It is a world-leading fabless semiconductor company that has transformed from a provider of 3D graphics chips into the dominant force in global Artificial Intelligence (AI) and Data Center computing.

I. Foundation and the Graphics Revolution (1993 – 2005)

In its early years, NVIDIA focused on bringing high-performance 3D graphics to personal computers.

• Technical Narrative:
  • 1995: NV1 Chip: Its first product, which attempted to integrate audio and graphics but faced setbacks because its “quadratic surfaces” technology did not become the industry standard.
  • 1999: GeForce 256: NVIDIA defined the GPU (Graphics Processing Unit). This chip moved “Transform and Lighting (T&L)” from the CPU to the GPU for the first time, fundamentally changing PC graphics.
• Business Development:
  • Jensen Huang has consistently argued that the rate of GPU performance improvement outpaces Moore’s Law, a view often described in the media as “Huang’s Law.”
  • Through rapid iteration, NVIDIA defeated its rival 3dfx and secured the contract for the first Xbox, establishing a duopoly in the gaming graphics card market.
• Revenue Levels:
  • 1999 (IPO Year): Revenue was approximately $158 million.
  • 2005: Revenue grew to approximately $2 billion, with the market almost entirely focused on PC gaming and enthusiasts.

 

II. The Awakening of CUDA and General-Purpose Computing (2006 – 2015)

This period marked NVIDIA’s most critical strategic pivot, expanding GPU applications from gaming to scientific research.

• Technical Narrative:
  • 2006: CUDA Platform: A parallel computing platform and programming model developed by NVIDIA. It allowed developers to use the massive parallel processing power of GPUs for general-purpose computing (GPGPU), widely used in science and engineering.
  • 2012: The Deep Learning Turning Point: Researchers discovered that NVIDIA GPUs had an overwhelming advantage in training neural networks (e.g., AlexNet), officially starting the AI era.
• Business Development:
  • This was a stage of “burning cash to build a moat.” Despite massive investment in CUDA with low initial returns, Jensen Huang insisted on embedding CUDA into every GPU shipped.
  • Business lines began to diversify: GeForce (Gaming), Quadro (Professional Visualization), and Tesla (Server Computing).
• Revenue Levels:
  • 2010: Revenue was approximately $3.3 billion, with growth slowing due to the financial crisis and challenges in the mobile chip market.
  • 2015: Revenue reached $4.68 billion. At this time, the Data Center business accounted for less than 10% of total revenue.

 

III. AI Explosion and Hardware-Software Integration (2016 – 2022)

NVIDIA’s dominance in AI training and inference was significantly established during this era.

• Technical Narrative:
  • Tensor Core (2017): Introduced specialized cores for deep learning optimization in the Volta architecture.
  • RTX Platform (2018): Combined Ray Tracing, Deep Learning, and Rasterization to provide realistic rendering, transforming content creation workflows.
  • NVIDIA DRIVE Platform: Covering autonomous vehicle technology from cars to data centers, supporting the development and deployment of self-driving systems.
• Business Development:
  • Vertical Integration Strategy: In 2019, NVIDIA acquired Mellanox for $7 billion, extending its reach into high-speed networking (Infiniband). This solved communication bottlenecks between chips in AI clusters.
  • Data Center revenue began to rival Gaming revenue, as NVIDIA evolved from a chip vendor to a platform provider.
• Revenue Levels:
  • 2017: Revenue was approximately $6.9 billion.
  • FY2022: Revenue leaped to $26.9 billion.

IV. Generative AI and System-Level Hegemony (2023 – 2025 Present)

With the explosion of Large Language Models (LLMs), NVIDIA became the hub of global computing power.

• • Technical Narrative:
    • • Blackwell Architecture (2024): Launched the B200 / GB200. The GB200 NVL72 integrates Blackwell GPUs with Grace CPUs, providing 30x the inference performance of the previous generation.
    • • Liquid Cooling and Interconnects: Led the “Liquid Cooling Era” and utilized NVLink 5.0 to achieve ultra-large-scale chip interconnectivity.

• • Business Development:
    • • From Chips to Racks: NVIDIA shifted from selling individual chips to selling complete “AI Factories” (e.g., NVL72 racks).
    • • Sovereign AI: Partnering with governments to build national-level computing power, opening new markets.

• • Revenue Levels (Explosive Growth):
    • • FY2024: Revenue reached $60.9 billion.
    • • FY2025: Total revenue reached $130.5 billion.
    • • Q3 FY2026 (Quarterly): Revenue hit a staggering $57.01 billion, YoY +62%, QoQ + 22%.

V. Supply Chain and Competitive Landscape (Latest 2025)

1. Supply Chain Ecosystem

• • Upstream: TSMC (4nm/3nm foundry, CoWoS advanced packaging), SK Hynix/Micron (HBM3e memory), ASE Technology

• • Midstream: Delta Electronics/Lite-On (Power management), AVC/Auras (Liquid cooling solutions), Chenbro (Server chassis).

• • Downstream: Foxconn/Quanta/Wistron (Server and rack assembly), ASUS/Acer (AI PCs and workstations).

2. Competitive Challenges

• • Traditional Rivals: AMD (Instinct MI325X/MI350) attempts to challenge with higher memory capacity.

• • Cloud Giants’ In-house Chips: Amazon (Trainium 2) and Google (TPU v5/v6) aim to reduce reliance on NVIDIA.

• • Emerging Challengers: Companies like Cerebras and Groq provide high-efficiency chips for specific inference scenarios.

3. Market Size and Share

As of 2023–2025, NVIDIA holds approximately 80% – 92% of the AI chip market share. Based on the report from MarketsandMarkets, the global AI chip market is expected to reach $565 billion by 2032, with a CAGR of approximately 16%.

VI. Current Industry Trends (2025 – 2030)

1. System Integration: “The Rack is the Unit”

In the coming years, the performance gains from a single chip alone will no longer be enough to satisfy the exponential demand for AI compute.

• • Trend: As demonstrated by the Blackwell GB200, NVIDIA is shifting its focus toward integrating CPUs, GPUs, DPUs, switches, and liquid cooling systems into a single, unified rack-scale system.

• • Business Impact: The industry is moving from traditional component assembly toward vertical integration. This shift has significantly boosted demand for advanced liquid cooling and high-power management solutions within the global supply chain (particularly for Taiwanese partners).

2. Edge AI and Physical AI

AI is moving beyond centralized data centers and into the physical world, powering robotics and autonomous vehicles.

• • Technical Trend: Products like the NVIDIA Thor chip and the Omniverse platform indicate that the industry is prioritizing “simulating the real world.” This allows robots to learn within digital twin environments before being deployed in actual physical factories.

• • Keywords: Humanoid Robotics, Software-Defined Vehicles (SDV).

3. Shifts in the Competitive Landscape: Custom Silicon and Open Ecosystems

While NVIDIA maintains a dominant market share, strong countervailing forces are emerging in the industry:

• • Custom Silicon (ASIC): Tech giants like Amazon, Google, and Microsoft are accelerating the development of their own AI accelerators (e.g., Trainium, TPU) to reduce costs and dependence on external suppliers.

• • Software Open-Sourcing: Platforms like AMD’s ROCm and Intel’s oneAPI are attempting to challenge the monopoly of the CUDA ecosystem, aiming to lower the barrier for developers to switch to alternative hardware.

4. The Tug-of-War Between Compute and Energy

The rapid expansion of AI is increasingly constrained by the availability of power.

• • Trend: The Performance-per-Watt ratio (Energy Efficiency) will become the most critical metric in the industry. Beyond 2025, a company’s ability to secure reliable power and integrate green energy into data centers will directly determine the growth ceiling of the AI industry.

Conclusion: NVIDIA’s Moat

NVIDIA’s success stems not only from powerful hardware (like Blackwell) but also from 20 years of deep cultivation in the CUDA software ecosystem and the technical barriers established through NVLink and Mellanox. It has formed a “Trinity” of competitive advantages across hardware, software, and networking.