Shawn Simmons, Investor Relations | May 10, 2017

WELCOME

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 1995. 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, 2017. Copies of reports we file with the SEC are posted on our website and are available from NVIDIA without charge. These forward-looking 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, 2017. 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.

3

AGENDA

Jensen Huang

Jeff Fisher

Shanker Trivedi

Rob Csongor

Deepu Talla

Colette Kress

All

Powering the AI Revolution

Gaming

Datacenter

Automotive

AI City

Financials

Q&A

Questions? Please email Shawn Simmons at ssimmons@nvidia.com

Jensen Huang, Founder & CEO

2017 INVESTOR DAY

6

RISE OF GPU COMPUTING

1980 1990 2000 2010 2020

102

103

104

105

106

107

GPU-Computing Perf 1.5X per year

1000X

by 2025

Single-threaded Perf

1.5X per year

1.1X per year

APPLICATIONS

SYSTEMS

ALGORITHMS

CUDA

ARCHITECTURE

7

NVIDIA PLATFORM

OpenGL, DirectX, CUDA

GRAPHICS AI HPC

Deep Learning Frameworks

NVIDIA GPU-computing SDK

Applications

NVIDIA DGX-1

IRAY MDL OPTIX

INDEX PHYSX

HAIR WATER FIRE

FLEX

MAESTRO CASTRO

Astrophysics

INCOMP3D

CFD

NekCEM Computational Electromagnetics

LSDalton

Quantum Chemistry

COSMO Climate Weather

Numeca

CFD

CloverLeaf

CFD

PowerGrid

Medical Imaging

8

NVIDIA POWERING THE AI REVOLUTION

ISAAC

NVIDIA GPU in Every Cloud

Xavier DLA

Open

Source DGX-1 and DGX Station Tesla V100

TensorRT

Tensor Core

NVIDIA

GPU CLOUD

CSPs

NVIDIA GPU Cloud

9

GROWTH DRIVERS

Gaming AI Self-Driving Cars

Jeff Fisher, SVP — GeForce

GAMING

400M Core PC Gamers

2B GAMERS WORLDWIDE

NALA EMEA APAC

Mobile

PC

Console

Mobile

PC

Console

WW GAMING MARKET OVER $100B

Source: DFC Intelligence – Game Revenue Source: NewZoo

$0

$20

$40

$60

$80

$100

$120

2016 2020

Mobile

PC

Console

Mobile

PC

Console

6% PC CAGR

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

FY 2016 FY 2017

Desktop Notebook

Pascal

Maxwell

Legacy

5YR CAGR INSTALLED BASE GAMING GPU $M

GEFORCE GAMING PLATFORM

REV 25%

ASP 12%

UNITS 11%

+43% Revenue

DEVELOPED + CHINA

+59% Revenue

EMERGING

44%

Source: NVIDIA

GEFORCE

EXPERIENCE

90M Clients

+ 15% YoY

27B Hours Played

+ 20% YoY

63 Game Ready Drivers

3X YoY

300M Gameplay Recordings

3X YoY

OPTIMIZE SHARE REWARDS

0

20

40

60

80

100

120

2016 2020

ESPORTS: GEFORCE FUELS NEW GENERATION

NEW GAMERS JOIN MARKET ESPORTS GROWTH CHOICE OF PROS

GeForce Powers ALL Major

Tournaments

GEFORCE BUYERS MOTIVATED BY ESPORTS

CHINA + EMERGING

75%

DEVELOPED

55%

MO

BA G

am

ers

(M

)

0

100

200

300

2016 2020

Esp

ort

s Audie

nce

(M)

2X

0

300

600

900

2016 2020

Esp

ort

s Advert

isin

g (

$M

)

3X

Source: DFC, NewZoo, NVIDIA

BIGGER, MORE BEAUTIFUL GAMES IB OPPORTUNITY VIRTUAL REALITY

FALL BLOCKBUSTERS

AAA: CINEMATIC GAMES DRIVE UPGRADE

% o

f IB

Above P

erf

Gam

e L

oad Index f

or

1080p @

60FPS

0.0

1.0

2.0

3.0

4.0

2013 2014 2015 2016 2017

$180

$140

0%

25%

50%

75%

100%

Overwatch Avg Load Scorpio

NEW HMDs

8%

11%

17%

18%

22%

GTX 980

GTX 980 Ti

GTX 970

GTX 1070

GTX 1080

VR GAMERS BUY HIGH END

81% of Gamers are

interested in VR

Gaming

*Estimates street price of graphics card based on actual GPU ASP Source: NVIDIA

*

*

0M

5M

10M

15M

0M

50M

100M

150M

2012 2013 2014 2015 2016

Gam

ing N

ote

book (M

U)

Tota

l Consu

mer

Note

book (

MU

)

Rela

tive P

ow

er

Eff

icie

ncy

0

2

4

6

8

10

Fermi2010

Kepler2012

Maxwell2014

Pascal2016

Student “Backpack” Notebook

Gaming GPU,

21mm, 5lbs, 15”

Consumer Notebook

Gaming Notebook

GEFORCE GAMERS WANT MOBILITY

NOTEBOOK EXPANDS GAMING MARKET 10X UNIT GROWTH

Source: NVIDIA, Gartner

Entry GPU

Consumer Notebook excludes Gaming Notebook

GEFORCE NOW: GEFORCE GAMING FOR ALL

Shanker Trivedi, SVP — Enterprise

DATACENTER

HIGHLIGHTS

Launched DGX AI Supercomputer

Launched Tesla Pascal AI/HPC

Datacenter Servers

450+ GPU Accelerated Applications

GPU in Every Public Cloud Service

Enterprise AI adoption accelerates

DATACENTER HAD A GREAT YEAR

FY 2017 FY 2016

$339M

$830M

145%

Q1 FY 2017

$143M

$409M

186%

Q1 FY 2018

YOY REVENUE GROWTH FY 2017 CUSTOMER MIX

Supercomputing

CSPs / ISPs

Enterprise

8X CUSTOMERS

Source: NVIDIA

INDUSTRY SOLUTIONS

Finance

Retail

Manufacturing

Internet

Healthcare

Energy

Federal

AVAILABLE EVERYWHERE

GPU AS A SERVICE

GPU SYSTEMS

ACCELERATE APPS

NVAIL

GTC

All AI Frameworks

Data Analytics

Enterprise Applications

Developer Outreach

Top 10 HPC Apps

COMPELLING DATACENTER STRATEGY

EXASCALE REQUIRES ACCELERATED HPC HPC EMBRACES AI

LEADERSHIP

CANDLE

TITECH

CORAL

CSCS

VALUE HPC TAM

0

2

4

6

8

10

2013 2020

8 ExaFLOPS

.6 ExaFLOPS

$4B

FP64 (HPC) FLOPS

# o

f Racks

(~30 K

W P

er

Rack)

0

4

8

12

16

20

32 CPUs +

64 P100s

576 CPUs

1 RACK ($0.6M)

8 RACKS ($11M)

19 RACKS ($14M)

VASP AMBER

SATURN V

Source: NVIDIA and publicly available data

RIKEN

1344 CPUs

$13M SAVINGS

GPUS ARE BEST FOR AI TRAINING REDUCE TRAINING TIME FROM WEEKS TO HOURS

TRAINING DATA

Language

Speech

Video

Image

VALUE DL TRAINING TAM

0

20

40

60

2013 2020

55 ExaFLOPS

1.4 ExaFLOPS

FP32 (DL TRAINING) FLOPS

0x

10x

20x

30x

40x

Tensorflow CNTK MXNet

Speed u

p c

om

pare

d t

o D

ual

Xeon

Source: NVIDIA and publicly available data; For 4 Yr Trend Chart: Relative speed-up of images/sec vs K40 in 2013. 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

500h SAVINGS $11B

12 h

3 wk

INFERENCING REQUIRES GPU ACCELERATION TRADITIONAL HYPERSCALE SERVERS CAN’T KEEP UP

INFERENCING

Video analytics

Translate

Search

VALUE DL INFERENCE TAM

INT8 (AI INFERENCE) EIOPs

Speak

0

100

200

300

400

500

2016 2020

450 EIOPs

50 EIOPs

50K Inference/sec

12 RACKS

$2.3M

1 RACK

$240k

Source: NVIDIA, publicly available data, inference using Resnet-50

$2M SAVINGS PER RACK $15B

FASTER REAL-TIME INFERENCING 10x

AI REVOLUTIONIZING INDUSTRIES

TREAT DETECT DIAGNOSE

HEALTHCARE

ECOSYSTEM GROWTH

Source: NVIDIA

2017

2,100 300

2016

GTM PARTNERS

2017

511,000

45,000

2012

DEVELOPERS

APPLICATIONS

2017

108

2012

460

STARTUPS

2017

300

2016

1,300

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

Rob Csongor, VP & GM — Automotive

THE RACE TO AI AUTONOMOUS VEHICLES

AI ENGAGEMENTS DRIVE PX 2 PARTNERS

REVENUE

0

50

100

150

200

250

FY17Q2 FY17Q3 FY17Q4 FY18Q1

NVIDIA AUTOMOTIVE GROWTH

0

100

200

300

400

500

600

FY13 FY14 FY15 FY16 FY17

Millions

52%

$487M

0

40

80

120

160

200

FY17Q2 FY17Q3 FY17Q4 FY18Q1

AUTONOMOUS VEHICLE GAME CHANGERS

AI SUPERCOMPUTING SoC

HD MAP DEEP LEARNING

PERCEPTION REASONING

HD MAP LOCALIZATION

DRIVING

AI COMPUTING

AI IS THE SOLUTION TO SELF-DRIVING

ADAS TO AUTONOMOUS VEHICLES (L3)

AC

CU

RA

CY

90%

80%

70%

100%

QTY OF DATA

DEEP LEARNING

TRADITIONAL COMPUTER VISION

99.99% HUMAN

ADAS

DETECTION DETECTION

LOCALIZATION

OCCUPANCY GRID

PATH PLANNING

VEHICLE DYNAMICS

OTA

L3

5X

ADAS TO AUTONOMOUS VEHICLES (L4)

DETECTION DETECTION

LOCALIZATION

OCCUPANCY GRID

PATH PLANNING

VEHICLE DYNAMICS

OTA

L3 L4

FAIL OPERATION

99.99…% AUTONOMY

SITUATIONAL AIs

URBAN DRIVING

8 Core Custom ARM64 CPU | 512 Core Volta GPU

Designed for ASIL D Functional Safety | 30 TOPS DL | 30W

XAVIER — 1ST AI CAR SUPERCHIP

ADAS

50X

5X

ADAS TO AI CAR

AI CAR

DETECTION DETECTION

LOCALIZATION

OCCUPANCY GRID

PATH PLANNING

VEHICLE DYNAMICS

OTA

L3

LIP READING

GAZE TRACKING

HEAD TRACKING

FACE RECOGNITION

L4

2X

50X

5X

NATURAL SPEECH

FAIL OPERATION

99.99…% AUTONOMY

SITUATIONAL AIs

URBAN DRIVING

ADAS

Deep learning is a new computing model that requires new methods, tools, and flow 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 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)

DRIVE PX Data Collection & Mapping Car

Data Factory

NVIDIA Mapworks Map Processing

NVIDIA DGX-1 Deep Learning Dev. System

OTA Server

NVIDIA DRIVE PX END TO END AI CAR COMPUTER PLATFORM

DRIVE PX Driving & Testing Car

FULL AND OPEN SW PLATFORM

1 TOPS

10 TOPS

100 TOPS

DPX 2 Parker Level 2/3

DPX 3 Xavier Level 4/5

Computer Vision Libraries

OS

Perception AI

CUDA, cuDNN, Tensor RT

Localization Path Planning

AI CAR PARTNER ANNOUNCEMENTS

HD MAPPING PARTNERS

AUTO PARTNERS

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

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

AI IS THE SOLUTION FOR AV

NVIDIA IS AT THE CENTER OF AI

Deepu Talla, VP & GM — TEGRA

AI CITY

AI CITY

Surveillance Forensics Law enforcement

Retail analytics Resource optimization Traffic management

EFFICIENCY

SAFETY

LA homeless population

SAFE AND SMART CITIES IS AN AI PROBLEM

0M

200M

400M

600M

800M

1,000M

2016 2020

1B installed security cameras WW (2020)

30B frames per day

Challenging real-world conditions

Traditional video analytics not trustworthy

74%

97%

2010 2016

Accura

cy

Image Classification

Human

Hand-coded CV

Deep Learning

AI achieves superhuman results

AI-driven intelligent video analytics

Parking entrance

Law enforcement

Traffic management

Airport security

CAMERA

ON-PREM SERVER 10s-100s of cameras

CLOUD 1000s of cameras

Resource optimization

Public safety

AI CITY NEEDS AN EDGE TO CLOUD ARCHITECTURE

NVIDIA METROPOLIS — EDGE TO CLOUD AI CITY PLATFORM

Camera

CLOUD Training and Inference

EDGE AND ON-PREMISES Inference

DGX

Video recorder Server

JETSON TESLA

JETPACK, TENSOR RT, DEEPSTREAM

NVIDIA AI CITY OPPORTUNITY

FY17 FY19 FY21

Series 1

~ $10M Revenue

“POCs”

$100M+ Revenue

<5% Streams AI-enabled

$2B TAM

10x

$1B Server

$1B Camera + Video recorder

NVIDIA AI CITY PLATFORM ADOPTION

10x speed-up in vehicle attribute classification

11x boost in investigation productivity

World-leading object detection

30x speed-up in people and attribute detection

6x improvement for pedestrian detection in rain

5x speed-up for ALPR Industry’s first search by example

30x faster than real-time video synopsis

NVIDIA AI CITY PARTNERS

Colette Kress, EVP & CFO

FINANCIALS

RECORDS

GROSS MARGIN

Gross Margin and Operating Income are Non-GAAP measures.

$6.9B

$5.0B

FY 2016 FY 2017 $1.0

$2.0

$3.0

$4.0

$5.0

$6.0

$7.0

$8.0

REVENUE

38%

56.8%

59.2%

FY 2016 FY 2017

240 BPS

OPERATING INCOME

2X

$1.1B

$2.2B

FY 2016 FY 2017

REVENUE GROWTH 3 YEAR CAGR ~20%

GROWTH

PLATFORMS

OEM & IP

~35% CAGR

AUTO

DATACENTER

PRO VIZ

GAMING

OEM & IP

FY 2014 FY 2017

$4.1B

$6.9B

~20% CAGR

MARKET PLATFORMS PRO VISUALIZATION

3-YEAR CAGR 2%

Millions

$789 $795 $750 $835

0

200

400

600

80011%

FY 2014 FY 2016 FY 2017 FY 2015

AUTO 3-YEAR CAGR ~70%

Millions

$99 $183

$320

$487

0

200

400

600

52%

FY 2014 FY 2016 FY 2017 FY 2015

DATACENTER 3-YEAR CAGR ~60%

Millions

$199 $317 $339

$830

0

300

600

900

145%

FY 2014 FY 2016 FY 2017 FY 2015

Billions

FY 2014 FY 2016 FY 2017 FY 2015

$1.5 $2.1

$2.8 $4.1

0

1

2

3

4

5

44%

GAMING 3-YEAR CAGR ~40%

GROSS MARGIN EXPANSION Value added platforms expand margins

Gross Profit and Gross Margin are Non-GAAP measures.

Billions

52%

54%

56%

58%

60%

0

1

2

3

4

5

FY 2014 FY 2015 FY 2016 FY 2017 Q1 FY 2018

59.6%

56.8%

59.2%

55.8%

55.1%

GM% Growth Platforms IP, PC & Tegra OEM

OPERATING EXPENSES YoY investments focused on AI

Operating Expenses is a Non-GAAP measure.

0

500

1000

Q2 FY 2017 Q4 FY 2017 Q1 FY 2018 Q3 FY 2017

Millions

$498

17% 11%

6%

$478 $448

$517

12%

0

1

2

FY 2014 FY 2016 FY 2017 FY 2015

$1.7

8%

$1.9 $1.7 $1.6 B

illions

$0.7 $1.0

$1.1

$2.2

$0.6

5%

15%

25%

35%

0.0

0.5

1.0

1.5

2.0

2.5

FY 2014 FY 2015 FY 2016 FY 2017 Q1 FY 2018

OPERATING MARGIN EXPANSION

Operating Income Operating Margin

Operating Income and Operating Margin are Non-GAAP measures.

16%

32% 33%

20%

Billions

22%

CASH AND CASH FLOW

$0.8

$3.3

$2.1

$1.7

$4.0

$2.2

0

2

4

6

Cash Flow Net Cash U.S. Cash

Cash flow increased over 100%; Net cash increased 21%

Billions

Net Cash = Total Cash – Total Debt; FY 2014 Total Debt = $1.37B; FY 2017 Total Debt = $2.82B

FY 2017 FY 2017 FY 2017 FY 2014 FY 2014 FY 2014

CAPITAL RETURN Since FY 2013: $4B ~85% FCF

Free Cash Flow is a Non-GAAP measure.

0

400

800

1,200

1,600

FY 2013 FY 2014 FY 2015 FY 2016 FY 2017 FY 2018

$1.0B

Intended Return Share Repurchase Dividend

$1.0B $1.1B

$147M

$0.8B

$1.25B

Millions

DRIVING SHAREHOLDER

VALUE

S&P 500 NASDAQ 100 NVIDIA

ROIC CY2016 11 % 13 % 20 %

Change since CY2013 +0.5 % +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

RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES

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 $2,274 (11) 8 (3) $2,268

55.1% (0.3) 0.2 (0.1) 54.9%

FY 2015 $2,611 (12) — — $2,599

55.8% (0.3) — — 55.5%

FY 2016 $2,846 (15) (20) — $2,811

56.8% (0.3) (0.4) — 56.1%

FY 2017 $4,088 (15) — (10) $4,063

59.2% (0.2) — (0.2) 58.8%

Q1 FY 2018 $1,154 (4) — — $1,150

59.6% (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.

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.

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 $1,610 126 32 4 $1,772

FY 2015 $1,657 146 37 — $1,840

FY 2016 $1,721 190 22 131 $2,064

FY 2017 $1,867 233 16 13 $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, compensation charges and other credits related to acquisitions.

C. Comprises of legal settlement costs, contributions, and restructuring and other charges.

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

Q2 FY 2016 $421 44 4 89 $558

Q3 FY 2016 $430 47 4 8 $489

Q4 FY 2016 $445 56 4 34 $539

Q1 FY 2017 $443 49 4 10 $506

Q2 FY 2017 $448 54 4 3 $509

Q3 FY 2017 $478 62 4 - $544

Q4 FY 2017 $498 68 4 - $570

Q1 FY 2018 $517 73 4 2 $596

RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.)

OPERATING MARGIN ($ IN MILLIONS & MARGIN PERCENTAGE)

NON-GAAP

STOCK-BASED

COMPENSATION

(A)

PRODUCT

WARRANTY

(B)

ACQUISITION-

RELATED ITEMS

(C)

OTHER

(D) GAAP

FY 2014 $664 (137) 8 (32) (7) $496

16% (3) — (1) — 12%

FY 2015 $954 (158) — (37) — $759

20% (3) — (1) — 16%

FY 2016 $1,125 (205) (20) (22) (131) $747

22% (4) — — (3) 15%

FY 2017 $2,221 (248) — (16) (23) $1,934

32% (4) — — — 28%

Q1 FY 2018 $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.

($ 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

RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.)

REVENUE BY MARKETS

($ IN MILLIONS) FY 2014 FY 2015 FY 2016 FY 2017 3 YR CAGR APPROXIMATE %S

Q1 FY2018

GAMING $1,511 $2,058 $2,818 $4,060 ~40% $1,027

PRO VISUALIZATION 789 795 750 835 --% 205

DATACENTER 199 317 339 830 ~60% 409

AUTO 99 183 320 487 ~70% 140

GROWTH

PLATFORMS $2,598 $3,353 $4,227 $6,212 ~35% $1,781

OEM & IP 1,532 1,329 783 698 ~-20% 156

TOTAL $4,130 $4,682 $5,010 $6,910 ~20% $1,937