NVIDIA — The Queen of AI Chips
Comprehensive analysis of NVIDIA: from founding to success, its products, models, achievements, and impact on the AI industry.
AI DayaHimour Team
April 10, 2026
NVIDIA didn’t invent artificial intelligence, but it built the infrastructure that runs on it. Every large language model, every AI agent, every generated image — behind it is mostly an NVIDIA processor. This position transformed within two years from a technical advantage into near‑monopoly leadership in technology’s fastest‑growing sector.
Founding and Path to AI
NVIDIA was founded in April 1993 in Santa Clara, California, by Jensen Huang, Chris Malachowsky, and Curtis Priem. The original goal was accelerating gaming graphics, with the GeForce 256 processor arriving in 1999 as the first commercially dedicated GPU.
Early 2006, the company launched the CUDA platform, allowing developers to program NVIDIA processors for diverse computational tasks beyond graphics. The decision that seemed purely technical at the time later became the foundational block of its dominance: when AI labs in 2010‑2015 needed parallel processors, CUDA was ready and familiar to researchers.
From Games to Artificial Intelligence
The real turning point came in 2012 when AlexNet — trained on NVIDIA processors — demonstrated a massive leap in image‑recognition accuracy. Academic researchers discovered that GPUs weren’t just for games; NVIDIA processors became the essential tool for training neural networks.
This insight led NVIDIA to develop AI‑specialized processors. Volta in 2017, then Turing, then Ampere, then Hopper A100 and H100 — each generation brought a substantial performance increase.
Blackwell Architecture and Current Leadership
The Hopper architecture with the H100 processor became the performance benchmark for AI labs since 2022. But Blackwell in 2024‑2025 raised the ceiling again. The GB200 processor delivers performance exceeding the previous generation by 10× in inference.
Financial results reflect this demand:
- Q3 of fiscal year 2026 (October 2025): $57 billion revenue, 62% year‑over‑year growth
- Q4 (January 2026): $68 billion revenue, 73% year‑over‑year growth
- Full‑year revenue for 2026: $215.9 billion, 65% increase from the previous year
The data‑center division — which includes AI processors — now represents over 90% of total revenue. “Blackwell sales are off the charts” was Jensen Huang’s phrase that came to summarize the market situation.
Demand Exceeds Supply
By April 2026, Blackwell systems are completely sold out until mid‑year with long waitlists from Meta, Microsoft, Amazon, and Google. The problem isn’t demand but manufacturing capacity — NVIDIA relies on TSMC as the exclusive manufacturer of its most advanced processors.
Projected numbers for 2026 exceed $320 billion in revenue from Blackwell alone.
Beyond Chips: Software and Integrated Ecosystem
CUDA isn’t just a software protocol; it’s an integrated ecosystem not easily escaped. Developers who learned CUDA are reluctant to transition to AMD or Intel alternatives even when technical parity becomes available.
NVIDIA shifts its competition from hardware to software: the NIM platform for model deployment, the DGX Cloud system for delivering AI computers via the cloud, and TensorRT for optimizing inference performance. This shift reinforces recurring revenue instead of total dependence on hardware‑sales cycles.
Competitors and Risks
AMD accelerates ROCm development as a CUDA alternative, but adoption remains limited. Google, Amazon, and Microsoft develop their own chips — TPU, Trainium, Azure Maia — to reduce dependence on NVIDIA. OpenAI struck a deal with AMD for 6 gigawatts of computing power.
U.S. export restrictions to China narrowed an important market window. Although NVIDIA developed toned‑down chips like the H20 compliant with restrictions, Chinese competitors like Huawei accelerate their HiBM alternative.
Next Architecture
At GTC 2026, NVIDIA unveiled the Rubin architecture built on TSMC’s 3nm process and HBM4 memory, promising a 10× reduction in inference cost compared to Blackwell. Rubin systems will begin shipping in the second half of 2026.
NVIDIA’s equation appears simple: as long as demand for AI computing doubles, and as long as switching to alternatives is complex and costly, the company remains in a position practically uncontested. The real challenge lies in what happens when software efficiency improvements in models reach a level that reduces demand for raw computing — a scenario sketched by the company’s stock falling 17% in a single day after the announcement of DeepSeek R1.
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