Nvidia Corporation stands out as a leader in technology, particularly in the realm of graphics processing units (GPUs). The company made its mark as the go-to source for gaming graphics chips and has since expanded into high-performance computing and artificial intelligence.
Nvidia operates as a fabless manufacturer. This means they design chips but rely on partners to produce them. For instance, the latest Nvidia Blackwell generation chips are produced by Taiwan Semiconductor Manufacturing Company (TSMC).
In its fiscal year 2025, which spans from February 2024 to January 2025, Nvidia reported a staggering revenue of $130.5 billion, marking a 114% increase compared to the previous year.
When it started, Nvidia focused on creating GPUs for PCs and quickly gained a reputation for the most powerful options available. Competing with ATI (later acquired by AMD), Nvidia’s GPUs are known for running games and 3D applications at impressive frame rates. They include NVENC video encoders and decoders to enhance video performance.
Recent Nvidia GPUs come packed with advanced features like ray tracing cores, Tensor Cores, and the Deep Learning Super Sampling engine, all of which enhance the capabilities of computer graphics.
Turning to artificial intelligence, Nvidia’s hardware has become central to the recent surge in machine learning and generative AI. GPUs are built for parallel tasks, allowing for trillions of operations per second. With its Compute Unified Device Architecture (CUDA), developers can tap into the full power of Nvidia’s GPUs efficiently, making them the preferred choice for AI and ML tasks.
Nvidia continually releases server chips tailored for AI, featuring high floating-point computing power and dedicated high-bandwidth memory. The A100 GPU, launched in 2020, played a crucial role in training modern generative AI models. Following that, the Hopper series, with chips like the H100 and H200, has been instrumental in developing large language models. The latest in this lineup, the Blackwell series, was introduced in 2024, with the GB200 Grace Blackwell Superchip bridging Grace CPUs and Blackwell GPUs.
In January 2025, Nvidia announced the Cosmos AI model, aimed at enhancing AI agents’ capabilities in the physical world. This is part of its Omniverse strategy, which connects digital and physical environments. Digital twins allow companies to create precise virtual replicas of real spaces; this aids in planning and efficiency, like simulating assembly lines before building them.
Nvidia’s history dates back to 1993 when Jen-Hsun “Jensen” Huang, Curtis Priem, and Chris Malachowsky founded the company in Santa Clara, California. They recognized the need for dedicated GPUs as gaming shifted from CPU reliance. The early ’90s saw Nvidia facing tough competition, but by 1999, the launch of the GeForce card changed the game with cutting-edge 3D graphics.
Nvidia grew as the GPU market consolidated around them and ATI. In 2006, they introduced CUDA, which allowed programmers to directly harness GPU power for various parallel computing tasks. They worked to integrate this platform into university curricula, resulting in a robust community of developers.
In 2008, Nvidia released the Tegra line, combining an Arm CPU with a GPU, initially aimed at automotive applications but later adopted by Nintendo for the Switch. The surge in cryptocurrency mining in 2016 led to a temporary GPU shortage, exacerbated by supply chain issues during the COVID-19 pandemic.
Nvidia has historically made strategic acquisitions, including Mellanox Technologies in 2019 for $6.9 billion, enhancing their capability in data processing. They once pursued acquiring Arm Holdings in a $40 billion deal, which ended due to regulatory hurdles.
By June 2024, Nvidia’s valuation surpassed $3 trillion, making it briefly the most valuable publicly traded company.
Nvidia’s product lineup is vast. The GeForce brand caters to consumers, while their enterprise architectures bear the names of renowned scientists like Maxwell and Turing. Core products include:
- GeForce: Graphic processors for desktops and laptops.
- Nvidia Quadro/RTX: Professional graphics solutions, with RTX replacing the retired Quadro line.
- Tegra: System-on-chip series for mobile devices.
- DGX servers: High-performance computing hardware.
- BlueField: Data processing units for efficient network management.
- Spectrum-X: Next-gen ethernet for data centers.
- Jetson: Compact solutions for embedded systems.
- DGX Spark: AI supercomputing systems.
- Nvidia AI Enterprise: Extensive software tools for enterprises.
- NIM (Nvidia Inference Microservices): A format for packaging AI models for deployment.
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