NVDA Preetam Jinka Akhil Kolluri Pavan Naik

Background Graphics processing units (GPUs) Chipsets Workstations Personal computers Mobile devices Servers Game consoles

Product line GeForce Quadro nForce Tegra

Competitors Intel AMD Texas Instruments

Intel Graphics built into the processors o GPU within CPU o Fairly new

AMD A semiconductor company - merged with ATI Very similar product line Nintendo GameCube, Wii, Xbox 360

Texas Instruments Currently make chips in Motorola Droid o Rumored to switch to Nvidia chips in Motorola phones now Motorola still plans to sell 12 to 14 million phones in 2010.

Charts

Low earnings / Lawsuit

Charts

Earnings Next earnings release: 11/12 Expected earnings: 0.143

The Numbers

Seasonal performance

CAPS 5-star rating

News GeForce GTX 580 3DTV Dell Tegra 2 Fastest Supercomputer

GeForce GTX 580 release?

SOCIAL RESPONSIBILITY

NVIDIA is at the forefront of solving some of the world’s most complex social and scientific problems. Disease diagnosis, environmental forecasting, surveillance for national security purposes, the search for clues along the far edge of the known universe — each is being addressed with tools accelerated through NVIDIA technology. Facilitating these breakthroughs is our core offering, the graphics processing unit, or GPU. NVIDIA’s GPU works in tandem with a central processing unit (CPU), and is a specialized tool that breaks down complex computing problems into a great many smaller tasks that run concurrently. Our parallel processors accelerate a CPU by times, enabling hundreds of billions of operations to occur each second. They turbocharge a system, rendering it more powerful and more efficient than a CPU alone. Parallel processing is helping researchers make quantum leaps in progress in fields ranging from climate modeling to medical tomography, yielding advances that were previously impossible due to technology limitations. Problems that once took days to solve now can be addressed in minutes. Computations that previously required large, costly, energy intensive CPU clusters can now be accomplished more efficiently and less expensively by GPUs. These advances are possible not only because we have invested billions of dollars in R&D, but also because we develop our products mindful of our ecosystem —our suppliers, partners and our end-customers, who are addressing enormously challenging problems. Ecosystem Approach NVIDIA’s ecosystem revolves around parallel processing and brings together a network of developers, researchers, students, scientists and other companies. We employ a substantial number of individuals who work with other companies to solve problems and develop new functionality. We work closely with researchers to understand the obstacles that prevent them from making progress, and we determine whether parallel processing could help. We connect researchers through our Fellowship programs and our CUDA Zone portal, and make software programs, such as Tesla BioWorkbench, that are designed to leverage our technology. Through technology and financial donations, we support a network of universities, professors and researchers who are solving important challenges through the GPU. In addition, we are engaged in efforts to reduce the power footprint of the GPU, focusing on improving our processors’ performance per watt of consumed power. We have made meaningful contributions through the deployment of Optimus, which intelligently optimizes a notebook computer’s battery life byshutting down the GPU when not in use—with no interference in usability or performance. Optimus reduces the GPU’s load on the battery and helps decrease the number of times it must be recharged, allowing for longer battery life. In addition, our Tegra system-on-a-chip, which is the size of a thumbnail, comprises eight separate processors that are activated only when required, enabling it to sip power. Designed for use in portable devices, it uses one-hundredth of the power traditionally used by desktop PC processors and one-tenth of the power of notebook processors.

Sources