NVIDIA Analyst Day April 10, 2008 Safe Harbor

Welcome to NVIDIA Analyst Day April 10, 2008 Safe Harbor

Except for the historical information contained herein, certain matters set forth in today’s presentation including, but not limited to, statements as to: visual computing; the discrete GPU, integrated graphics and mobile devices; optimization of the PC; our strategies; our growth and growth factors; market opportunities; the features, benefits, capabilities and performance of our current and future products and technologies and consumer demands and expectations as well as other predictions and estimates are forward-looking statements within the meaning of the Private Securities Reform Act of 1995. These forward-looking statements and any other forward-looking statements that go beyond historical facts that are made today during the presentation, demonstrations or in response to questions are subject to risks and uncertainties that may cause actual results to differ materially. For a complete discussion of risk factors that could affect our present and future results, please refer to our Annual Report on Form 10-K for the fiscal year ended January 27, 2008 and from time to time in the reports we file with the Securities and Exchange Commission. All forward-looking statements are made as of today, based on the information currently available to us. Except as required by law, we assume no obligation to update any such statements. NVIDIA The Visual Computing Company First, graphics that we have all come to know and love today, I have news for you. It's coming to an end. Our multi-decade old 3D graphics rendering architecture that's based on a rasterization approach is no longer scalable and suitable for the demands of the future.

Pat Gelsinger keynote, IDF Shanghai

The Graphics Industry

GPU 59%

GPU Shipments 366 Million

INTEL IGP 41%

Source: Mercury Research The Graphics Industry

GPU 59%

CPU Shipments GPU Shipments 273 Million 366 Million

INTEL IGP 41%

Source: Mercury Research The Graphics Industry

GPU

CPU Shipments 73% 273 Million

73 Million Idle IGPs INTEL IGP 27%

Source: Mercury Research Probably be no need [to purchase a dedicated graphics card in a short while].

Ron Fosner, Intel Graphics and Gaming Technologist, Shanghai IDF, 2008

10x IGP performance by 2010

Paul Otellini, Intel Analyst Day, March 2008 50

30 Intel 965g Intel G35 25 GeForce 9500GT 20 GeForce 9600GT GeForce 9800GTX 15

10 10x by 2010

0 FAIL Core 2 Duo E6550 No AA 3DMark06 Doom3 CoD4 No Aniso Filter Industry Truth: The Creator of Unreal Tournament 2004 says Intel is “incapable of running modern games.”

Intel’s integrated graphics just don't work. I don't think they will ever work.

All the Intel integrated graphics are still incapable of running any modern games.

Tim Sweeney President Epic Games March 10, 2008 Intel GMA 3100 Can’t Properly Run Unplayable or Games with Problems 2/3 of Top Selling Games Sims2 Sim City 5 Call Of Duty 4 Half Life 2 Civilization IV Enemy Territory: Quake Wars Crysis Battlefield 2 Command & Conquer 3 Unreal Tournament 3 Battlefield 2142 The Witcher Bioshock Halo: Combat Evolved Guild Wars: Nightfall Medieval II: Total War Configuration: Microsoft Vista 32-bit World In Conflict Intel G33: Intel 15.8.0.1437 drivers Heroes Of Might & Magic V Core2 Duo, 2GB Memory Supreme Commander *based on NPD retail sales reports NVIDIA #1 with Gamers

Source: Steam survey of 1.5M users NVIDIA Lead Growing through DirectX 10

Source: Steam survey, DX10 users only Multi-core processors [could] handle life-like animations, such as weather or effects better than dedicated GPUs. For instance, multi-core processors can handle the graphics tasks in a better manner than a high-end graphics board could ever do.

Ron Fosner, Intel Graphics and Gaming Technologist, Shanghai IDF, 2008 3000%

2500%

2000%

1500%

1000% Relative 3DMark06 Performance3DMark06 Relative 500% GMA 3100 Core 2 Duo E4400 0% $113 $163 $240 $342 $999 Average Total GPU + CPU Spend Benchmark run at 1280x1024, 4xAA/8x AF. Upgrade CPU, Graphics Constant 3000%

2500%

2000%

1500%

1000% Relative 3DMark06 Performance3DMark06 Relative 500% GMA 3100 Core 2 Duo Core 2 Duo E6550 Core 2 Quad Q6600 Core 2 Quad QX9650 E4400 0% $113 $163 $240 $342 $999 Average Total GPU + CPU Spend Benchmark run at 1280x1024, 4xAA/8x AF. GPU Delivers 27x the Bang For Buck

Upgrade CPU, Graphics Constant Upgrade GPU, CPU Constant 3000% GeForce 8800 GT

2500%

2000%

GeForce 8600 GT 1500%

1000%

GeForce 8400 GS Relative 3DMark06 Performance3DMark06 Relative 500% GMA 3100 Core 2 Duo Core 2 Duo E6550 Core 2 Quad Q6600 Core 2 Quad QX9650 E4400 0% $113 $163 $240 $342 $999 Average Total GPU + CPU Spend Benchmark run at 1280x1024, 4xAA/8x AF. See Appendix for system specs. Today’s Core2 Platform Next Gen Repackaging Optimized for Visual Computing

PCIe Discrete Notebook Unit Share

12,000 100%

90%

80% 9,000 70%

60%

6,000 50%

40%

30% 3,000 20%

10%

0 0% FQ1'08 FQ2'08 FQ3'08 FQ4'08 FQ1'09 FQ2'09 FQ3'09 FQ4'09

NVIDIA TAM Market Share Historical data from Mercury Research FQ1’09 onward NVIDIA estimates Discrete Notebook Revenue Share

$300 100%

90%

80% $225 70%

60%

$150 50%

40%

30% $75 20%

10%

$- 0% FQ1'08 FQ2'08 FQ3'08 FQ4'08 FQ1'09 FQ2'09 FQ3'09 FQ4'09

NVIDIA TAM Market Share Historical data from Mercury Research FQ1’09 onward NVIDIA estimates We are seeing a global movement to design PCs that are optimized for how we use them Visual Computing

Enterprise Computing

1990s Today 1990s Today Optimized PC Advertisement from Best Buy circular January 27 issue, p21 Shockwaves

Gateway Dell HP Sony P-6831 FX XPS M1530 dv9700t CR490EBR

Price $1249 $1299 $1289 $1324

CPU 1.67GHz 2GHz 1.83GHz 2.5GHz Core 2 Duo Core 2 Duo Core 2 Duo Core 2 Duo

GPU 8800 GTS 8400 GS 8400 GS Intel Integrated

Performance 8000 1600 1600 500 3DMark06

3DMark06 estimates based on NVIDIA testing of similar graphics configurations Prices available online “… features exceptional graphics performance as its main focus …”

“ … to give you high definition movie pleasure and smooth gaming fun …”

“… superior graphic quality and HDMI display output allows for entertainment enjoyment in high definition.”

Source: ASUS NVIDIA The Visual Computing Company GPU Growth Driven by Insatiable Visual Computing Demand

120% 14% Growth

110%

100% 7% Decline

90%

Rekative Revenue Growth Relative Revenue Growth Revenue Relative

80% 2005 2007 CPU GPU Source: Mercury Research Programmable GPU Enables Heterogeneous Computing

4 cores

Multi-Core plus Many-Cores Heterogeneous Computing Multi-Core plus Many-Core

Core 2 Duo GeForce E8400 8800 GTS (Dual Core) (Many-Core)

# of Cores 2 128

# of GFLOPS 48 576 The Power of Heterogeneous Computing

Interactive visualization of Ionic placement for Transcoding HD video Isotropic turbulence Astrophysics nbody volumetric white matter molecular dynamics stream to H.264 for simulation in Matlab using simulation5 connectivity1 simulation on GPU2 portable video3 .mex file CUDA function4

Financial simulation of GLAME@lab: An M-script Ultrasound medical Highly optimized object Cmatch exact string LIBOR Model with API for Linear Algebra imaging for cancer oriented molecular matching to find similar swaptions6 Operations on GPU7 diagnostics8 dynamics9 proteins and gene sequences10 Computer Industry Luminaries Driving the Heterogeneous Computing Movement

“Many applications can run orders of magnitude faster on a heterogeneous CPU+GPU system today. CUDA has been shown to be a very effective programming model for heterogeneous computing.” - Wen-Mei Hwu, University of Illinois at Urbana-Champaign

“One reason to have different „sized‟ processors in a many-core architecture is to improve parallel speedup ....” - David Patterson, Berkeley CUDA GPUs GPU CPU

Heterogeneous Computing

Oil & Gas Finance Medical Biophysics Numerics Audio Video Imaging

Speed Power Many-Core ILLIAC IV Maspar Blue Gene Cray-1 Thinking Machines

Multi-Core DEC PDP-1 IBM System 360 IBM POWER4 Intel 4004 VAX Many-Core Computing

4 cores 128 cores 512 cores

4 cores Many-Core Thread Management

80 GigaBytes/sec To Data Application Software Industry Standard C Language

Numerics Engine FFT BLAS CuDPP

System CUDA Compiler CUDA Tools 1U PCI-E Switch C Fortran Debugger Profiler

4 cores nbody Demo on CPU and GPU

300

250

200

150

100

0 CPU1 mGPU2 Mobile3 Desktop4 GPU GPU

Source: NVIDIA See appendix for system specs CUDA Platforms

512-Core 1U System 128-Core Processor Card

8-Core mGPU Speed Power Power Performance - VMD

14 Billions of Evaluations 12

Per Watt 10

0 V8 QX6850 E6850 3x8800 2x8800 8800 Ultra 8800 Ultra 8800 GTX 8800 GTS 8600 GTS 8600 GT 8500 GT 8400 GS GTX GTX eVGA

http://www.hardware.fr/articles/678-8/nvidia-cuda-plus-pratique.html Bob Keller President and CEO, Silicon Informatics AutoDock: Software Used for Drug Discovery

AutoDock: Finds ways to fit or dock small molecules into proteins Tries millions of different configurations

Used for virtual screening of new drug leads Determine if drug molecules can dock into proteins of bacteria Like finding the right key out of a pile of millions of different keys

Used by thousands of institutions worldwide

Authored by Scripps Research Institute Accelerating AutoDock with CUDA

Silicon Informatics created siAutoDock Implemented key kernels in CUDA

National Cancer Institute reports 12x speedup Run went from 2 hours to 10 minutes

Speed-up expected to scale linearly with multiple GPUs

“We can only hope that in the long run, Silicon Informatics' efforts will accelerate the discovery of new drugs to treat a wide range of diseases, from cancer to Alzheimer's, HIV to malaria.” Dr. Garrett Morris, Scripps, Author of AutoDock John Michalakes Lead Software Developer Weather Research and Forecast (WRF) Model National Center for Atmospheric Research Weather Modeling

One of the first HPC applications

Continues to have highest direct public impact Accurate & timely severe storm/hurricane prediction Air pollution & dispersion modeling at urban scales

Ever hungry for cycles WRF Hurricane Katrina Forecast 5% of Top500® systems dedicated to weather / climate 4km resolution moving west

Weather modeling requires Solving bigger problem sizes -- solved today by building bigger clusters Solving problems faster -- improvements tailing off with conventional processors CUDA Results

Weather Research and Forecast (WRF) model 4000+ registered users worldwide First-ever release with GPU acceleration

Adapted 1% of WRF code to CUDA

Resulted in 20% overall speedup

12km CONUS WRF benchmark Ongoing work to adapt larger percentage of Running on NCSA CUDA cluster WRF to CUDA Rahm Shastry President and CEO, Nascentric SPICE Simulation

SPICE: Among oldest software tools in electronic design automation

Fundamental to semiconductor circuit design & verification Simulates transistors, interconnects & parasitics in a chip NVIDIA’s chips have close to 1 billion transistors

Demand for speedup has led to variants with less accuracy FastSPICE: faster, but less accurate

Accurate SPICE simulation takes weeks to months on a workstation OmegaSim and OmegaSim GX

OmegaSim: Parallel CPU implementation of SPICE models by Nascentric

OmegaSim GX: GPU implementation in CUDA Speedup transistor evaluation ~40X Up to 90% of SPICE execution time spent in transistor evaluation 8x overall speedup

OmegaSim GX OmegaSim

4 CPUs 32 CPUs + > 4 GPUs faster Ahmet Karakas President and CEO, Gauda Wafer Yield Problem

desired image compensated mask Growing Market $10B in Correctable Yield Losses

Growing TAM Requirements Exceed CPU Capacity

$1B 900 Design $800M

800 Fab/IDM 700 600 $240M 500 400 300 200 100 0 2007 2008 2009 20010 20011 Gauda’s Solution: 200x Faster and Lower Cost

Time-to-Money

CPU days

1000’s 100%

CPUs Revenue hours FPGA 10’s $ GPUs 60%

Time Time-to Delay $100K $1 M $10 M Market (3mo.) Cost Typical 1 Yr Life-Cycle Gerald Hanweck, PhD Principal, Hanweck Associates, LLC Quantitative Finance Dilemma

As financial products and processes have grown in complexity...

• Credit Default Swaps (CDS) • Algorithmic Trading • Collateralized Debt Obligations (CDOs) • High-Frequency Trading • Asset/Mortgage-Backed Securities (ABS/MBS) • Program Trading • Structured Finance • Risk Management • Asset Valuation

...their computational needs have become more demanding...

• Monte Carlo simulations • Numerical optimization • Binomial / trinomial trees & lattices • Finite-difference / finite-element methods • Numerical integration • Digital signal processing • Matrix algebra • Real-time data processing

...and compute-related resources are at a premium:

• Shrinking IT budgets • Productivity pressure • Limited availability of server rack space • Regulatory pressure • Energy costs for servers and cooling • “Green” pressure

HANWECK ASSOCIATES, LLC The GPU Solution

Raw computational speed for improved productivity: • A current generation NVIDIA Tesla GPU has 128 floating-point cores. • 50-100x performance increase over a single CPU core with NVIDIA GPU  Option pricing (binomial tree): 50x speedup (1.25m/sec vs. 25k/sec)  Monte Carlo simulation: 100x speedup (30m/sec vs. 300k/sec)  Numerical integration (Heston model): 100x speedup (5k/sec vs. 50/sec)

Increased efficiency in real-estate and power consumption: • The GPU’s higher performance translates to:  Lower up-front hardware spend  Lower IT charges for rack space and maintenance  Lower power consumption  Lower operating costs HANWECK ASSOCIATES, LLC Savings Case Study

Case Study: Hanweck Associates Volera™ real-time option valuation engine

Capable of valuing the entire U.S. listed options market in real-time using 3 NVIDIA Tesla S870’s

GPUs CPUs GPU savings Number of Processors 12 600 Rack Space 6U 54U 9x Hardware Spend $42,000 $262,000 6x Annual Cost $140,000 $1,200,000 9x

Figures assume: • NVIDIA Tesla S870s with one 8-core host server per unit • CPUs are 8-core blade servers; 10 blades per 7U • $1,800/U/month rack and power charges • 5-year depreciation HANWECK ASSOCIATES, LLC OptiTex Fashion & Textile Design Software Sam Blackman, CEO [email protected] Elemental’s Solution

. RapiHD™ Video Platform: Software that harnesses the GPU • First company to leverage key GPU technology trends for video . RapiHD™ eliminates the need for specialized hardware • Disruptive technology for the entire video industry Why the GPU?

. Elemental recognized three key trends: 1. GPUs have become much more programmable 2. GPUs have become immensely powerful

3. CPU and GPU communication is no longer a bottleneck GIGAFLOPS Architectural Fit

. CUDA GPUs will revolutionalize video processing . Video compression divides frames in blocks of pixels . CPU processes these serially; GPU processes them in parallel Heterogeneous Computing Video Processing

GeForce Core 2 Duo 8800 GTS T5450 (Many Core)

Number of cores 2 128 Blu-ray HD decoding (CPU utilization) > 100% ~ 28% H.264 encoding (normalized performance) 1 19x

Source: NVIDIA See appendix for details Era of Visual Computing

Programmable Graphics CUDA – DX11

Programmable Shaders DX8 – DX9 – DX10

Fixed-Function Pipelines “3D Accelerators”

G80 Next RIVA 128 GeForce 3 Tesla2 GeForce 6 CUDA Gen

1997 2001 2005 2007 2008 2009 August Photoreal Rendering

Parallel Processing Imaging & Sensing

Dimensionalization Rich Environments Fracture Soft Shadows

Detailed Characters

Ambient Occlusion Indirect Lighting Subsurface Scatter

Optical Complexity Caustics Participating Media Fluids mental ray Photorealism in Motion Pictures

SPEED RACER Image rendered with mental ray® by Digital Domain © 2008 Warner Brothers. All Rights Reserved. Image © and courtesy Digital Domain Image © and courtesy Digital Domain Image © and courtesy RTT AG “So far I haven’t seen a compelling example for using pure classical ray tracing… Both methods [rasterization and ray tracing] have been in the race for some time, but rasterization is significantly ahead based on real world efficiencies.” Cevat Yerli, Crytek

“Head to head rasterization is just a vastly more efficient use of whatever transistors you have available.” John Carmack, ID Software

http://www.pcper.com/article.php?aid=532 And…there’s much more than RT

Geometry Synthesis Subdivision Surfaces The Future of Visual Computing

Programmable and Specialized Processing

Graphics and C/C++

Ray Tracing and Rasterization

Rendering and Simulation

…Evolution

PhysX

140 Titles

25,000 Active Users

All Platforms Industry’s Most Advanced Physics Engine

Powerful Core Physics Engine

Rigid Body Dynamics Collision Detection Anti-tunneling, Joints, Springs and Motors

Advanced Dynamics

Cloth Metallic Deformation Soft Bodies Force Fields Physics Shaders Smooth Particle Hydrodynamics Heterogeneous Computing Physics Processing

GeForce Core 2 Quad 9800 GTX (Quad Core) (Many Core)

# of Cores 4 128 Particles 1 20x Fluid 1 6x Soft Bodies 1 5x Cloth 1 5x Source: NVIDIA estimates See appendix for system specs GPU PhysX

Complete port in one month via CUDA!

Rabid CUDA adoption by GPU PhysX ecosystem

Exponential increase in developer adoption Visual computing is much more than chips – it’s about the user experience Create Experiences – Not Chips Power for Playing Games, Cool Operation When Watching a Movie

Low Power High Performance

250

Surfing the web 200

150

100 Total System Total Playing a Game

Power Consumption Power 50

- Source: NVIDIA Playing Bluray Time See Appendix for system specs NVIDIA Experience on Any CPU The World’s Most Affordable Vista Premium PC

Celeron+ Via CN+Geforce G945+ICH4

1+8 cores 1 core 36 GFLOPS 6.4 GFLOPS

Vista Premium

Blu-ray HD

DX10

Cost <$45 <$45

Source: See Appendix The Next Personal Computer Revolution The Next Personal Computer Revolution is Starting

Enabled by computing technologies

Architected for extreme low power

Uncompromising computing and visual experience > 1 Billion Units Per Year

Data from IDC, Gartner, ABI, IDC, In-Stat and NVIDIA estimates NVIDIA’s Computer on a Chip

500 man years and culmination of 15 years of innovation

Most advanced ultra-low power computer ever built

The mobile device will become our most personal computer World’s Smallest Visual Computer World’s Smallest Visual Computer

GeForce GPU

CPU

HD HD

ISP

AVP memory

(…CPU Included…) GPU - Poised to be a Disruptive Technology

2007 PC TAM 2012 PC TAM $34B $53B

Source: Mercury Research, NVIDIA Appendix I

Slides 22-24. Benchmarks run on Asus P5K-V motherboard (Intel G33 based) with 2GB DDR2 system memory using Windows Vista Ultimate. Intel chipset driver is 17.14.10.1283. NVIDIA graphics driver is 174.00.

Slide 41. Sources: 1. Interactive Visualization of Volumetric White Matter Connectivity in DT-MRI Using a Parallel-Hardware Hamilton-Jacobi Solver paper by Won-Ki Jeong, P. Thomas Fletcher, Ran Tao and Ross T. Whitaker 2. GPU Acceleration of Molecular Modeling Applications paper. 3. Video encoding uses iTunes on the CPU, and Elemental on the GPU running under Windows XP. CPUs tested were Intel Core 2 Duo 1.66GHz and Intel Core 2 Quad Extreme 3GHz. GPUs tested were GeForce 8800M on the Gateway P-Series FX notebook, and GeForce 8800 GTS 512MB. CPUs and GeForce 8800 GTS 512 were run on Asus P5K-V motherboard (Intel G33 based) with 2GB DDR2 system memory. Based on an extrapolation of 1 min 50 sec 1280x720 HD movie clip. http://developer.nvidia.com/object/matlab_cuda.html 4. High performance direct gravitational nbody simulations on graphics processing units paper. Communicated by E.P.J. van den Heuvel 5. LIBOR paper by Mike Giles and Su Xiaoke. 6. FLAG@lab: An M-script API for Linear Algebra Operations on Graphics Processors paper 7. http://www.techniscanmedicalsystems.com/ 8. General Purpose Molecular Dynamics Simulations Fully Implemented on Graphics Processing Units paper by Joshua A. Anderson, Chris D. Lorenz and A. Travesset 9. Fast Exact String Matching On the GPU presentation by Michael C. Schatz and Cole Trapnell

Slide 51. Benchmarks: 1. Nbody on GeForce 8800GTS 512MB and nbody on the CPU both ran on a Sun Ultra24 workstation with one Intel Core2 Extreme Q6850 3Ghz, with 3GB memory. 2. Nbody on motherboard graphics benchmark was run on GeForce 8200 chipset-based motherboard. CPU was AMD Phenom 9600 Quad-Core 2.3Ghz with 2GB memory. Appendix II

Slide 77. Benchmarks: 1. “Art of Disney” sees consistent dropped frames with CPU decode, indicating that decode requires more CPU power than is available. CPU usage in same system is only 28% when GPU performs decode. System specs: Intel Core 2 Duo T5550 (1.8GHz), GeForce 8800 GTS, 2 GB DRAM on an Intel 965-based motherboard. 2. 19x transcode result based on comparing iTunes on a Core 2 Duo T5450 (1.67GHz) versus Elemental Technology's RapiHD using GeForce 8800 GTS, both running under Windows XP. At present audio is not being performed by RapiHD. Source video was the same for both, a 1min:50s clip, 1280x720p MPEG2 transcoded to MPEG4 for iPod. Same resulting resolution (640x360) and the same data rates and frames per second in both. Audio decode is a very small workload compared to HD video decode, and its omission from part of this test is not likely to make a material difference to the result.

Slide 91. Benchmarks: 1. PhysX CPU estimates based on a system with a Core 2 Quad Q6700. 2. GPU estimates based on PhysX running on one GeForce 9800 GTX with the graphics rendering on a separate GeForce 9800 GTX 3. Both running Windows XP Pro

Slide 95. Benchmarks run on: Phenom 9500 quad core, GeForce 9800GX2, 024MB Corsair DDR2-800, Seagate 7200.10 160GB, Vista Enterprise, NVIDIA 174.91 and nForce 18.11 drivers.

Slide 97. As of June 2008, “Vista Premium” certification will require DirectX 10 support; 945G is not DirectX 10 capable. Blu-ray playback requires HDCP support not present in 945G chipset. Market prices based on checks with customers. Legal information

Performance tests and ratings are measured using specific computer systems and/or components and reflect the approximate performance of NVIDIA products as measured by those tests. Any difference in system hardware or software design or configuration may affect actual performance. Buyers should consult other sources of information to evaluate the performance of systems or components they are considering purchasing.

Copyright © 2008 NVIDIA Corporation. All rights reserved. NVIDIA, The NVIDIA logo, GEFORCE, HYBRIDPOWER, SLI, TESLA, CUDA, TEGRA and PUREVIDEO are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and/or other countries.

Other names and brands may be claimed as the property of others.