WELCOME. Shawn Simmons, Investor Relations May 10, 2017

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1 WELCOME Shawn Simmons, Investor Relations May 10, 2017

2 Safe Harbor Forward-Looking Statements Except for the historical information contained therein, certain matters in these presentations including, but not limited to, statements as to: our investments, market opportunities and TAM; our growth; future financial results, estimates and forecasts; the performance, benefits and availability of our products and technologies; our strategies; and other predictions and estimates are forward-looking statements within the meaning of the Private Securities Litigation Reform Act of These forward-looking statements and any other forward-looking statements that go beyond historical facts that are made in these presentations are subject to risks and uncertainties that may cause actual results to differ materially. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners products; design, manufacturing or software defects; changes in consumer preferences and demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems and other factors. For a complete discussion of factors that could materially affect our financial results and operations, please refer to the reports we file from time to time with the SEC, including our Form 10-K for the fiscal year ended January 29, Copies of reports we file with the SEC are posted on our website and are available from NVIDIA without charge. These forwardlooking statements are not guarantees of future performance and speak only as of May 10, 2017, based on information currently available to us. Except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. Financial Measures These presentations contain historical revenue amounts for certain of our product lines and businesses which provide investors with additional information to supplement the segment reporting information contained in our Form 10-K for the fiscal year ended January 29, In addition to U.S. GAAP financials, these presentations include certain non-gaap financial measures. These non-gaap financial measures are in addition to, and not a substitute for or superior to, measures of financial performance prepared in accordance with U.S. GAAP. See the Appendix for a reconciliation between each non-gaap measure and the most comparable GAAP measure. Where we present non-gaap financial measures, including non-gaap gross profit, gross margin, operating expense, operating income, operating margin, and free cash flow, we generally exclude stock-based compensation, legal settlement, net warranty charges or release, acquisition-related costs and other expense, where applicable. 2

3 AGENDA Powering the AI Revolution Gaming Datacenter Automotive AI City Financials Q&A Jensen Huang Jeff Fisher Shanker Trivedi Rob Csongor Deepu Talla Colette Kress All Questions? Please Shawn Simmons at 3

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5 2017 INVESTOR DAY Jensen Huang, Founder & CEO

6 RISE OF GPU COMPUTING APPLICATIONS ALGORITHMS SYSTEMS CUDA ARCHITECTURE Single-threaded Perf GPU-Computing Perf 1.5X per year 1.5X per year 1.1X per year X by

7 GRAPHICS NVIDIA PLATFORM HPC Applications AI Deep Learning Frameworks NekCEM Computational Electromagnetics COSMO Climate Weather CloverLeaf CFD MAESTRO CASTRO Astrophysics IRAY MDL OPTIX LSDalton Quantum Chemistry Numeca CFD PowerGrid Medical Imaging INCOMP3D CFD NVIDIA GPU-computing SDK PHYSX FLEX INDEX NVIDIA DGX-1 HAIR WATER FIRE OpenGL, DirectX, CUDA 7

8 NVIDIA POWERING THE AI REVOLUTION NVIDIA GPU CLOUD NVIDIA GPU in Every Cloud CSPs NVIDIA GPU Cloud Tesla V100 Tensor Core TensorRT DGX-1 and DGX Station Xavier DLA Open Source ISAAC 8

9 GROWTH DRIVERS Gaming AI Self-Driving Cars 9

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11 GAMING Jeff Fisher, SVP GeForce

12 2B GAMERS WORLDWIDE WW GAMING MARKET OVER $100B 400M Core PC Gamers $120 $100 $80 $60 Console PC 6% PC CAGR Console PC Mobile $40 Mobile Mobile Mobile NALA EMEA APAC $20 $0 PC PC Console Console Source: NewZoo Source: DFC Intelligence Game Revenue

13 GEFORCE GAMING PLATFORM GAMING GPU $M 5YR CAGR INSTALLED BASE $4,000 $3,500 $3,000 $2,500 44% REV 25% ASP 12% Legacy Pascal $2,000 $1,500 UNITS 11% Maxwell $1,000 $500 $- FY 2016 FY 2017 Desktop Notebook DEVELOPED + CHINA +43% Revenue EMERGING +59% Revenue Source: NVIDIA

14 GEFORCE EXPERIENCE 90M Clients + 15% YoY 27B Hours Played + 20% YoY 63 Game Ready Drivers 3X YoY OPTIMIZE SHARE REWARDS 300M Gameplay Recordings 3X YoY

15 Esports Advertising ($M) Esports Audience (M) MOBA Gamers (M) ESPORTS: GEFORCE FUELS NEW GENERATION ESPORTS GROWTH NEW GAMERS JOIN MARKET CHOICE OF PROS X GeForce Powers ALL Major Tournaments X Source: DFC, NewZoo, NVIDIA GEFORCE BUYERS MOTIVATED BY ESPORTS CHINA + EMERGING 75% DEVELOPED 55%

16 Game Load Index for 60FPS % of IB Above Perf AAA: CINEMATIC GAMES DRIVE UPGRADE BIGGER, MORE BEAUTIFUL GAMES IB OPPORTUNITY VIRTUAL REALITY % 75% 81% of Gamers are interested in VR Gaming 3.0 $180 * 50% 25% NEW HMDs 2.0 0% Overwatch Avg Load Scorpio 1.0 FALL BLOCKBUSTERS VR GAMERS BUY HIGH END GTX % GTX % $140 * GTX 970 GTX 980 Ti GTX 980 8% 11% 17% *Estimates street price of graphics card based on actual GPU ASP Source: NVIDIA

17 Relative Power Efficiency Total Consumer Notebook (MU) GEFORCE GAMERS WANT MOBILITY NOTEBOOK EXPANDS GAMING MARKET 10X UNIT GROWTH Student Backpack Notebook Gaming GPU, 21mm, 5lbs, 15 Entry GPU 150M 100M 50M Consumer Notebook Gaming Notebook 15M 10M 5M Gaming Notebook (MU) 0 Fermi 2010 Kepler 2012 Maxwell 2014 Pascal M Consumer Notebook excludes Gaming Notebook 0M Source: NVIDIA, Gartner

18 GEFORCE NOW: GEFORCE GAMING FOR ALL

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20 DATACENTER Shanker Trivedi, SVP Enterprise

21 DATACENTER HAD A GREAT YEAR YOY REVENUE GROWTH HIGHLIGHTS FY 2017 CUSTOMER MIX $339M FY % $830M FY 2017 Launched DGX AI Supercomputer Launched Tesla Pascal AI/HPC Datacenter Servers 450+ GPU Accelerated Applications 8X CUSTOMERS $143M 186% Q1 FY 2017 $409M Q1 FY 2018 GPU in Every Public Cloud Service Enterprise AI adoption accelerates Supercomputing CSPs / ISPs Enterprise Source: NVIDIA

22 COMPELLING DATACENTER STRATEGY ACCELERATE APPS INDUSTRY SOLUTIONS AVAILABLE EVERYWHERE Top 10 HPC Apps All AI Frameworks Data Analytics Enterprise Applications Developer Outreach GTC NVAIL Federal Energy Healthcare Internet Manufacturing Finance Retail GPU AS A SERVICE GPU SYSTEMS

23 # of Racks (~30 KW Per Rack) EXASCALE REQUIRES ACCELERATED HPC HPC EMBRACES AI HPC TAM $4B LEADERSHIP CORAL VALUE $13M SAVINGS ExaFLOPS.6 ExaFLOPS FP64 (HPC) FLOPS CSCS RIKEN TITECH SATURN V CANDLE CPUs + 64 P100s 1 RACK ($0.6M) 576 CPUs VASP 8 RACKS ($11M) 1344 CPUs AMBER 19 RACKS ($14M) Source: NVIDIA and publicly available data

24 Speed up compared to Dual Xeon GPUS ARE BEST FOR AI TRAINING REDUCE TRAINING TIME FROM WEEKS TO HOURS DL TRAINING TAM TRAINING DATA VALUE $11B Image 500h SAVINGS ExaFLOPS Video 40x 30x 12 h 20 Speech 20x ExaFLOPS FP32 (DL TRAINING) FLOPS Language 10x 0x 3 wk Tensorflow CNTK MXNet Source: NVIDIA and publicly available data; For 4 Yr Trend Chart: Relative speed-up of images/sec vs K40 in AlexNet training throughput based on 20 iterations. Training with Resnet-50 on DL frameworks, all data measured, run with real data set For GPU, use driver r375_00, cudnnv6, NCCL 1.6.1, max batch size per GPU For CPU, use latest Intel Caffe + MKL, use batch size 128

25 INFERENCING REQUIRES GPU ACCELERATION TRADITIONAL HYPERSCALE SERVERS CAN T KEEP UP DL INFERENCE TAM INFERENCING VALUE $15B Search $2M SAVINGS PER RACK EIOPs Speak 10x FASTER REAL-TIME INFERENCING 200 Translate 50K Inference/sec EIOPs INT8 (AI INFERENCE) EIOPs Video analytics 12 RACKS $2.3M 1 RACK $240k Source: NVIDIA, publicly available data, inference using Resnet-50

26 AI REVOLUTIONIZING INDUSTRIES HEALTHCARE DETECT DIAGNOSE TREAT

27 ECOSYSTEM GROWTH APPLICATIONS STARTUPS , DEVELOPERS GTM PARTNERS 511,000 2,100 45, Source: NVIDIA

28 DATACENTER SUMMARY $30B opportunity, acceleration is disruptive Best value performance & energy efficiency AI optimized solution stack, available everywhere HPC & AI market leadership Enterprises adopting AI NVIDIA Volta, Tesla V100, DGX-1, and GPU Cloud increases momentum

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30 THE RACE TO AI AUTONOMOUS VEHICLES Rob Csongor, VP & GM Automotive

31 Millions NVIDIA AUTOMOTIVE GROWTH REVENUE DRIVE PX 2 PARTNERS AI ENGAGEMENTS $487M % FY13 FY14 FY15 FY16 FY17 0 FY17Q2 FY17Q3 FY17Q4 FY18Q1 0 FY17Q2 FY17Q3 FY17Q4 FY18Q1

32 AUTONOMOUS VEHICLE GAME CHANGERS DEEP LEARNING HD MAP AI SUPERCOMPUTING SoC

33 AI IS THE SOLUTION TO SELF-DRIVING PERCEPTION REASONING DRIVING HD MAP LOCALIZATION AI COMPUTING

34 ACCURACY ADAS TO AUTONOMOUS VEHICLES (L3) 100% 99.99% HUMAN DEEP LEARNING 90% 5X OTA VEHICLE DYNAMICS PATH PLANNING OCCUPANCY GRID 80% 70% TRADITIONAL COMPUTER VISION LOCALIZATION DETECTION DETECTION ADAS L3 QTY OF DATA

35 ADAS TO AUTONOMOUS VEHICLES (L4) URBAN DRIVING XAVIER 1 ST AI CAR SUPERCHIP 8 Core Custom ARM64 CPU 512 Core Volta GPU Designed for ASIL D Functional Safety 30 TOPS DL 30W 50X SITUATIONAL AIs % AUTONOMY OTA FAIL OPERATION 5X VEHICLE DYNAMICS PATH PLANNING OCCUPANCY GRID LOCALIZATION DETECTION DETECTION ADAS L3 L4

36 ADAS TO AI CAR NATURAL SPEECH 2X FACE RECOGNITION HEAD TRACKING GAZE TRACKING URBAN DRIVING LIP READING 50X SITUATIONAL AIs % AUTONOMY OTA FAIL OPERATION 5X VEHICLE DYNAMICS PATH PLANNING OCCUPANCY GRID LOCALIZATION DETECTION DETECTION ADAS L3 L4 AI CAR

37 DRIVE PX Data Collection & Mapping Car Data Factory NVIDIA DRIVE PX END TO END AI CAR COMPUTER PLATFORM Deep learning is a new computing model that requires new methods, tools, and flow NVIDIA Mapworks Map Processing NVIDIA DGX-1 Deep Learning Dev. System AV requires an end-to-end development flow from sensor architecture, data collection & processing, mapping, DNN model & driving algorithm development, in-car AI computing, testing, to OTA OTA Server DRIVE PX Driving & Testing Car NVIDIA is developing a full-stack open-platform drivable L3/L4 self-driving car, connected to all HD maps, continuously driving/testing/refining, and with open SDKs for customers to develop their own DNNs and driving algorithms NVIDIA created one single workflow and architecture scalable from L3 (DPX2) to L4 (DPX3)

38 FULL AND OPEN SW PLATFORM 100 TOPS Localization Path Planning DPX 3 Xavier Level 4/5 Perception AI 10 TOPS DPX 2 Parker Level 2/3 Computer Vision Libraries 1 TOPS CUDA, cudnn, Tensor RT OS

39 AI CAR PARTNER ANNOUNCEMENTS AUTO PARTNERS HD MAPPING PARTNERS

40 NVIDIA AI CAR OPPORTUNITY BY 2025 TAM $ OPPORTUNITY Self-Driving Cars AI L4/L5 5 Million Vehicles $5 Billion Self-Driving Cars AI L2+/L3 15 Million Vehicles $2 Billion AI Co-Pilot 5 Million Vehicles $1 Billion Source: ABI Research

41 AI IS THE SOLUTION FOR AV NVIDIA IS AT THE CENTER OF AI KEY TAKEAWAYS NVIDIA STRATEGIES: End-to-end deep learning system Full stack and open AI car platform One architecture, from L2+ to L3 to L4 to AI Car Rich ecosystem

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43 AI CITY Deepu Talla, VP & GM TEGRA

44 AI CITY LA homeless population EFFICIENCY Traffic management Retail analytics Resource optimization SAFETY Surveillance Law enforcement Forensics

45 Accuracy SAFE AND SMART CITIES IS AN AI PROBLEM 1,000M 800M Image Classification Human 97% 600M 400M 74% Deep Learning 200M 0M Hand-coded CV B installed security cameras WW (2020) 30B frames per day Challenging real-world conditions Traditional video analytics not trustworthy AI achieves superhuman results AI-driven intelligent video analytics

46 CLOUD 1000s of cameras Resource optimization Public safety ON-PREM SERVER 10s-100s of cameras AI CITY NEEDS AN EDGE TO CLOUD ARCHITECTURE Traffic management Airport security CAMERA Parking entrance Law enforcement

47 NVIDIA METROPOLIS EDGE TO CLOUD AI CITY PLATFORM EDGE AND ON-PREMISES Inference CLOUD Training and Inference JETPACK, TENSOR RT, DEEPSTREAM Camera Video recorder Server JETSON TESLA DGX

48 NVIDIA AI CITY OPPORTUNITY Series 1 $2B TAM $1B Server $1B Camera + Video recorder ~ $10M Revenue POCs 10x $100M+ Revenue <5% Streams AI-enabled FY17 FY19 FY21

49 NVIDIA AI CITY PLATFORM ADOPTION Industry s first search by example 30x faster than real-time video synopsis 6x improvement for pedestrian detection in rain 5x speed-up for ALPR 10x speed-up in vehicle attribute classification 11x boost in investigation productivity 30x speed-up in people and attribute detection World-leading object detection

50 NVIDIA AI CITY PARTNERS

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52 FINANCIALS Colette Kress, EVP & CFO

53 RECORDS $8.0 REVENUE GROSS MARGIN OPERATING INCOME $7.0 $6.9B 56.8% 240 BPS 59.2% $2.2B $6.0 $5.0 $5.0B 38% 2X $4.0 $1.1B $3.0 $2.0 $1.0 FY 2016 FY 2017 FY 2016 FY 2017 FY 2016 FY 2017 Gross Margin and Operating Income are Non-GAAP measures.

54 REVENUE GROWTH 3 YEAR CAGR ~20% $4.1B GROWTH PLATFORMS ~20% CAGR ~35% CAGR $6.9B AUTO DATACENTER PRO VIZ GAMING OEM & IP OEM & IP FY 2014 FY 2017

55 Millions Millions Billions Millions MARKET PLATFORMS GAMING 3-YEAR CAGR ~40% PRO VISUALIZATION 3-YEAR CAGR 2% $1.5 $2.1 $2.8 44% $4.1 FY 2014 FY 2015 FY 2016 FY $789 $795 $750 11% $835 FY 2014 FY 2015 FY 2016 FY 2017 DATACENTER 3-YEAR CAGR ~60% AUTO 3-YEAR CAGR ~70% % % $199 $317 $339 $830 FY 2014 FY 2015 FY 2016 FY $99 $183 $320 $487 FY 2014 FY 2015 FY 2016 FY 2017

56 Billions GROSS MARGIN EXPANSION Value added platforms expand margins 5 GM% Growth Platforms IP, PC & Tegra OEM 59.2% 59.6% 60% 4 58% % 55.8% 56.8% 56% % 0 FY 2014 FY 2015 FY 2016 FY 2017 Q1 FY % Gross Profit and Gross Margin are Non-GAAP measures.

57 Billions Millions OPERATING EXPENSES YoY investments focused on AI 2 8% $1.6 $1.7 $1.7 $ % 11% 12% 17% $448 $478 $498 $ FY 2014 FY 2015 FY 2016 FY 2017 Q2 FY 2017 Q3 FY 2017 Q4 FY 2017 Q1 FY 2018 Operating Expenses is a Non-GAAP measure.

58 Billions OPERATING MARGIN EXPANSION 2.5 Operating Income Operating Margin 32% 33% 35% % 22% 25% % $2.2 15% 0.5 $0.7 $1.0 $1.1 $ FY 2014 FY 2015 FY 2016 FY 2017 Q1 FY % Operating Income and Operating Margin are Non-GAAP measures.

59 Billions CASH AND CASH FLOW Cash flow increased over 100%; Net cash increased 21% 6 4 $4.0 $3.3 2 $1.7 $2.1 $2.2 $0.8 0 FY 2014 FY 2017 FY 2014 FY 2017 FY 2014 FY 2017 Cash Flow Net Cash U.S. Cash Net Cash = Total Cash Total Debt; FY 2014 Total Debt = $1.37B; FY 2017 Total Debt = $2.82B

60 Millions CAPITAL RETURN Since FY 2013: $4B ~85% FCF 1,600 Share Repurchase Dividend Intended Return $1.25B 1,200 $1.1B $1.0B $1.0B 800 $0.8B 400 $147M 0 FY 2013 FY 2014 FY 2015 FY 2016 FY 2017 FY 2018 Free Cash Flow is a Non-GAAP measure.

61 S&P 500 NASDAQ 100 NVIDIA ROIC CY % 13 % 20 % DRIVING SHAREHOLDER VALUE Change since CY % +0.4 % +12 % Change in Invested Capital 14 % 41 % 47 % Total Shareholder Return 64 % 95 % 842 % Source: Company Filings, Thomson Reuters Notes ROIC: CY2013 and CY2016. NVIDIA's corresponding ROIC as of FY2014 and FY2017 respectively ROIC = NOPAT / Total Debt + Shareholders' Equity TSR: February 1, 2013 to January 31, 2017 Invested Capital = Total Debt + Shareholders Equity

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63 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES

64 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES GROSS MARGIN ($ IN MILLIONS & MARGIN PERCENTAGE) NON-GAAP STOCK-BASED COMPENSATION (A) PRODUCT WARRANTY (B) OTHER (C) GAAP FY 2014 FY 2015 FY 2016 FY 2017 Q1 FY 2018 $2,274 (11) 8 (3) $2, % (0.3) 0.2 (0.1) 54.9% $2,611 (12) $2, % (0.3) 55.5% $2,846 (15) (20) $2, % (0.3) (0.4) 56.1% $4,088 (15) (10) $4, % (0.2) (0.2) 58.8% $1,154 (4) $1, % (0.2) 59.4% A. Stock-based compensation charge was allocated to cost of goods sold. B. Consists of the release of warranty reserve balance and warranty charge associated with a product recall. C. Consists of legal settlement and other related costs.

65 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.) OPERATING EXPENSES ($ IN MILLIONS) NON-GAAP STOCK-BASED COMPENSATION (A) ACQUISITION- RELATED ITEMS (B) OTHER (C) GAAP FY 2014 $1, $1,772 FY 2015 $1, $1,840 FY 2016 $1, $2,064 FY 2017 $1, $2,129 A. Stock-based compensation charge was allocated to research and development expense, and sales, general and administrative expense. B. Consists of amortization of acquisition-related intangible assets, transaction costs, and other credits related to acquisitions. C. Comprises of legal settlement costs, contributions, and restructuring and other charges.

66 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.) OPERATING EXPENSES ($ IN MILLIONS) NON-GAAP STOCK-BASED COMPENSATION (A) ACQUISITION- RELATED ITEMS (B) OTHER (C) Q2 FY 2016 $ $558 Q3 FY 2016 $ $489 Q4 FY 2016 $ $539 Q1 FY 2017 $ $506 Q2 FY 2017 $ $509 Q3 FY 2017 $ $544 Q4 FY 2017 $ $570 Q1 FY 2018 $ $596 GAAP A. Stock-based compensation charge was allocated to research and development expense, and sales, general and administrative expense. B. Consists of amortization of acquisition-related intangible assets, transaction costs, compensation charges and other credits related to acquisitions. C. Comprises of legal settlement costs, contributions, and restructuring and other charges.

67 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.) OPERATING MARGIN ($ IN MILLIONS & MARGIN PERCENTAGE) FY 2014 NON-GAAP STOCK-BASED COMPENSATION (A) PRODUCT WARRANTY (B) ACQUISITION- RELATED ITEMS (C) OTHER (D) GAAP $664 (137) 8 (32) (7) $496 16% (3) (1) 12% FY 2015 FY 2016 FY 2017 Q1 FY 2018 $954 (158) (37) $759 20% (3) (1) 16% $1,125 (205) (20) (22) (131) $747 22% (4) (3) 15% $2,221 (248) (16) (23) $1,934 32% (4) 28% $637 (77) (4) (2) $554 33% (4) 29% A. Stock-based compensation charge was allocated to cost of goods sold, research and development expense, and sales, general and administrative expense. B. Consists of the release of warranty reserve balance and warranty charge associated with a product recall. C. Consists of amortization of acquisition-related intangible assets, transaction costs, and other credits related to acquisitions. D. Comprises of legal settlement costs, contributions, and restructuring and other charges.

68 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.) ($ IN MILLIONS) FY 2013 FY 2014 FY 2015 FY 2016 FY 2017 Q1 FY2018 GAAP net cash flow provided by operating activities $824 $835 $905 $1,175 $1,672 $282 Purchase of property and equipment and intangible assets (183) (255) (122) (86) (176) (53) Free cash flow $641 $580 $783 $1,089 $1,496 $229

69 REVENUE BY MARKETS ($ IN MILLIONS) FY 2014 FY 2015 FY 2016 FY YR CAGR APPROXIMATE %S Q1 FY2018 GAMING $1,511 $2,058 $2,818 $4,060 ~40% $1,027 PRO VISUALIZATION % 205 DATACENTER ~60% 409 AUTO ~70% 140 GROWTH PLATFORMS $2,598 $3,353 $4,227 $6,212 ~35% $1,781 OEM & IP 1,532 1, ~-20% 156 TOTAL $4,130 $4,682 $5,010 $6,910 ~20% $1,937

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