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Shawn Simmons, Investor Relations | May 10, 2017
WELCOME
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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.
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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 [email protected]
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Jensen Huang, Founder & CEO
2017 INVESTOR DAY
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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
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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
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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
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9
GROWTH DRIVERS
Gaming AI Self-Driving Cars
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Jeff Fisher, SVP — GeForce
GAMING
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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
$120
2016 2020
Mobile
PC
Console
Mobile
PC
Console
6% PC CAGR
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$-
$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
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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
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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
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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
*
*
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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
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GEFORCE NOW: GEFORCE GAMING FOR ALL
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Shanker Trivedi, SVP — Enterprise
DATACENTER
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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
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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
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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
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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
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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
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AI REVOLUTIONIZING INDUSTRIES
TREAT DETECT DIAGNOSE
HEALTHCARE
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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
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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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Rob Csongor, VP & GM — Automotive
THE RACE TO AI AUTONOMOUS VEHICLES
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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
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AUTONOMOUS VEHICLE GAME CHANGERS
AI SUPERCOMPUTING SoC
HD MAP DEEP LEARNING
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PERCEPTION REASONING
HD MAP LOCALIZATION
DRIVING
AI COMPUTING
AI IS THE SOLUTION TO SELF-DRIVING
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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
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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
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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
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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
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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
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AI CAR PARTNER ANNOUNCEMENTS
HD MAPPING PARTNERS
AUTO PARTNERS
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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
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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
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Deepu Talla, VP & GM — TEGRA
AI CITY
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AI CITY
Surveillance Forensics Law enforcement
Retail analytics Resource optimization Traffic management
EFFICIENCY
SAFETY
LA homeless population
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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
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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
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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
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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
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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
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NVIDIA AI CITY PARTNERS
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Colette Kress, EVP & CFO
FINANCIALS
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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
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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
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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%
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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
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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
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$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%
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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
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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
$1.25B
Millions
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DRIVING SHAREHOLDER
VALUE
S&P 500 NASDAQ 100 NVIDIA
ROIC CY2016 11 % 13 % 20 %
Change since CY2013 +0.5 % +0.4 % +12 %
Change in Invested Capital 14 % 41 % 47 %
Total Shareholder Return 64 % 95 % 842 %
Source: Company Filings, Thomson Reuters
Notes
ROIC: CY2013 and CY2016. NVIDIA's corresponding ROIC as of FY2014 and FY2017 respectively
ROIC = NOPAT / Total Debt + Shareholders' Equity
TSR: February 1, 2013 to January 31, 2017
Invested Capital = Total Debt + Shareholders Equity
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RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES
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RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES
GROSS MARGIN ($ 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) — — $1,150
59.6% (0.2) — — 59.4%
A. Stock-based compensation charge was allocated to cost of goods sold.
B. Consists of the release of warranty reserve balance and warranty charge associated with a product recall.
C. Consists of legal settlement and other related costs.
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A. Stock-based compensation charge was allocated to research and development expense, and sales, general and administrative expense.
B. Consists of amortization of acquisition-related intangible assets, transaction costs, and other credits related to acquisitions.
C. Comprises of legal settlement costs, contributions, and restructuring and other charges.
RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.)
OPERATING EXPENSES ($ 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 $2,129
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A. Stock-based compensation charge was allocated to research and development expense, and sales, general and administrative expense.
B. Consists of amortization of acquisition-related intangible assets, transaction costs, compensation charges and other credits related to acquisitions.
C. Comprises of legal settlement costs, contributions, and restructuring and other charges.
RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.)
OPERATING EXPENSES ($ 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 $596
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RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.)
OPERATING MARGIN ($ 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) $554
33% (4) — — — 29%
A. Stock-based compensation charge was allocated to cost of goods sold, research and development expense, and sales, general and administrative expense.
B. Consists of the release of warranty reserve balance and warranty charge associated with a product recall.
C. Consists of amortization of acquisition-related intangible assets, transaction costs, and other credits related to acquisitions.
D. Comprises of legal settlement costs, contributions, and restructuring and other charges.
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($ 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 $229
RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.)
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REVENUE BY MARKETS
($ 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
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