INVESTOR UPDATE. September 2018

Transcription

1 INVESTOR UPDATE September 2018

2 SAFE HARBOR Forward-Looking Statements Except for the historical information contained herein, certain matters in this presentation including, but not limited to, statements as to: the world s demand for computing power growing exponentially; NVIDIA s ability to advance computing, including by optimizing across the entire stack, and the rate of advancing computing; NVIDIA s growth into multiple markets, including through the use of its unified architecture; the availability of NVIDIA GPUs and Jetson AGX Xavier DevKit; NVIDIA GPUs accelerating deep learning frameworks and applications; the benefits, impact, performance and abilities of: NVIDIA s computing platform, the Turing architecture, RT Core, ray tracing, Tensor Core, GeForce, GeForce RTX, Quadro RTX, RTX Servers, NVIDIA s GPUs and accelerated computing platform, DGX, inference performance, TensorRT, DGX-2, HGX-2, Xavier, AGX, DRIVE AGX, Jetson AGX, Jetson AGX Xavier, Clara AGX, DRIVE, DRIVE AGX, DRIVE AGX DevKit, NVIDIA Isaac, Clara AGX Xavier, GPU Servers, and our products, technologies, services, platforms and programs; our market and industry opportunities and TAM; DGX being the world s most advanced AI platform; shorter training time equaling faster progress in advancing AI; NVIDIA s ability to master inference; every industry tapping into the power of AI; DGX-2 being the world s most powerful AI system for data center deployments; the value of accelerated datacenter performance; Xavier enabling automation in many large industries; NVIDIA s full stack enabling market leaders to take advantage of our platform; the entities developing on DRIVE and creating the future of transportation; the next chapter of AI being autonomous machines; NVIDIA tackling big opportunities in some of the largest markets; accelerated computing being the way forward as Moore s Law has ended; NVIDIA s unified architecture driving growth and margin expansion across markets; our growth and growth drivers; our strategies; market trends; future financial results, estimates and forecasts; how NVIDIA innovates; our production roadmap and schedules; 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 this presentation 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 products 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 annual report on Form 10-K and quarterly reports on Form 10-Q. 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 the date hereof, 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 This presentation contains historical revenue amounts for certain of our market platforms and businesses which provides investors with additional information to supplement the segment reporting information contained in our Form 10-K for the fiscal year ended January 28, In addition to U.S. GAAP financials, this presentation includes 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 our website 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 margin, non-gaap operating margin, and free cash flow we generally exclude stock-based compensation, product warranty charge, restructuring costs, and other charges, where applicable. Free cash flow is calculated as GAAP net cash provided by operating activities less purchases of property and equipment and intangible assets.

3 ACCELERATED COMPUTING 1000X EVERY 10 YEARS The world s demand for computing power continues to grow exponentially, yet CPUs are no longer keeping up as Moore s Law has ended. NVIDIA pioneered accelerated computing to solve this challenge. Optimizing across the entire stack from silicon to software allows NVIDIA to advance computing in the post-moore s Law era for large and important markets: GPU PERFORMANCE CPU PERFORMANCE Gaming, Pro Viz, High Performance Computing (HPC), AI, Cloud, Transportation, Healthcare, Robotics, AI IOT

4 GROWING INTO MULTIPLE LARGE MARKETS Gaming +59% Datacenter (HPC, Cloud, AI) +77% Pro Viz +21% Auto +9% $4,000 $3.5B $1,600 $600 $400 $1.5B $532M $306M $3,000 $1,200 $450 $300 $2,000 $800 $300 $200 $1,000 $400 $150 $100 $0 1H FY18 1H FY19 $0 1H FY18 1H FY19 $0 1H FY18 1H FY19 $0 1H FY18 1H FY19 Revenue in millions. 1H FY18 and 1H FY19 represents the first six months of each fiscal year.

5 3 YEARS OF GROWTH AND MARGIN EXPANSION Revenue Up 2.5X Gross Margin Up ~700 bps Operating Margin Up ~2000 bps Free Cash Flow Up 2.9X $14 $12 $10 70% 50% $12.5B 43% 40% 65% 64% $4 $3 $3.1B $8 $6 $5.0B 60% 57% 30% 20% 22% $2 $1.1B $4 $2 55% 10% $1 $0 FY16 Annualized FY19 50% FY16 1H FY19 0% FY16 1H FY19 $0 FY16 Annualized FY19 Gross margin, operating margin, and FCF are non-gaap measures. 1H FY19 represents first six months of the fiscal year. Annualized FY19 revenue and FCF is calculated based on Q2 FY19 results multiplied by four.

6 ONE ARCHITECTURE, EIGHT LARGE MARKETS GAMING HPC TRANSPORTATION HEALTHCARE Three types of chips GPU, SOC, and NVSwitch. Three types of systems GTX/RTX for graphics cards, DGX for AI/HPC, AGX for AI machines. Accelerated computing requires an end-to-end stack, vertical market expertise, and ecosystem partners. PRO VIZ AI ROBOTICS AI IOT ACCELERATION STACKS One architecture leverages scale and investment to address large, growing markets.

7 1M DEVELOPERS ON NVIDIA COMPUTING PLATFORM DEVELOPERS Best platform > more developers > more apps > more customers. NVIDIA GPUs are available in every cloud, from every systems maker. Accelerate every deep learning framework; 600 applications, including top 15 HPC apps. GTC Attendees 7X in 5 Yrs CUDA Downloads 5X in 5 Yrs 25K 8M USERS

8 TURING A GIANT LEAP FOR GRAPHICS AND AI NVIDIA invents RTX hybrid rendering model that combines rasterization, ray tracing, and AI. Two new processors make it possible RT Core and Tensor Core. New RT Core achieves holy grail of computer graphics real-time ray tracing. 120 TURING 9X PEAK FLOPS 114 New multi-precision Tensor Core is a giant leap for AI inference up to 12X Pascal. TENSOR CORE RT CORE Advanced shaders deliver up to 2X speed-up for traditional rasterized games. SHADER COMPUTE PASCAL TITAN Xp FP32 INT32 TC FP32 INT32 TC TURING 2080 Ti

9 GEFORCE RTX RESETS $100B GAMING MARKET GeForce RTX is the biggest GPU leap in 10 years first real-time ray tracing GPU, first 100+ TFLOPS GPU, first neural graphics processing. First to enable 60Hz gaming on 4K monitors. 4K 60FPS GTX 980 Ti GTX 980 FASTEST GAMING GPU PLAYS AT 4K 60FPS GTX 1080 Ti GTX 1080 MAXWELL PASCAL TURING RTX 2080 Ti RTX K 60FPS DEEP LEARNING IMAGING WITH 114 TFLOPS TENSOR CORE GTX 980 Ti GTX 980 DEEP LEARNING IMAGING GTX 1080 Ti GTX 1080 MAXWELL PASCAL TURING RTX 2080 Ti RTX 2080 GeForce RTX delivers 2X performance boost with Deep Learning Super Sampling (DLSS, a new AI-enabled antialiasing technique), and up to 6X for ray-traced graphics. Over 30 games support RTX including Battlefield V, Shadow of the Tomb Raider, and Metro Exodus. RTX and rising GPU requirements help outgrow gaming market.

10 RTX OPENS $250B VISUAL EFFECTS INDUSTRY PHOTOREAL VFX RTX-ACCELERATED RENDERERS Quadro RTX delivers photoreal graphics that creators didn t expect for another 5-10 years. GPUs can now accelerate photoreal rendering for large industries that previously only used CPU server farms 1.5M CPU servers worldwide. DESIGN VISUALIZATION AEC FILM & TELEVISION RTX Server with 8 GPUs can replace 60 dual-cpu servers reducing cost, space, and power. RTX-ACCELERATED RENDER FARM: 1/4 THE COST, 1/10 THE SPACE, 1/11 THE POWER Millions of servers, workstations, and PCs estimated $50B of computing are in use today for photoreal rendering.

11 NVIDIA GPUS POWER NEW SUPERCOMPUTERS IN $10B HPC MARKET 17 OF TOP 20 MOST ENERGY-EFFICIENT DELIVERED 56% OF NEW TOP500 PERFORMANCE IN % NVIDIA powers five of top seven supercomputers, 17 of top 20 most energy-efficient. 11% 25% Five of the six 2018 Gordon Bell prize finalists did their work on the new NVIDIA GPU-accelerated Summit and Sierra systems Tesla K Tesla P Tesla V100 Delivered 56% of new performance on the latest Top500 list. Accelerate ~100 of the Top500 4X expansion opportunity. 5 OF WORLD S TOP 7 MOST POWERFUL #1 Summit: 122 PF #3 Sierra: 72 PF #5 ABCI: 20 PF #6 Piz Daint: 20 PF #7 Titan: 18 PF

12 WORLD S MOST ADVANCED AI PLATFORM IMAGES TRANSLATION AI training is the ultimate HPC challenge. FASTEST SINGLE CHIP FASTEST SINGLE NODE FASTEST AT SCALE FASTEST SINGLE NODE FASTEST AT SCALE NVIDIA DGX is a reference full-stackoptimized system designed for AI, HPC, and big data analytics. 24 hours 108 minutes 6.6 minutes 5 hours 32 minutes Shorter time to train equals faster progress in advancing better and more accurate AI. DGX-2 WORLD S LARGEST GPU: 2 PFLOPS, 512GB HBM2 Images: Single Chip: Resnet-50 V1 Training on Tesla V100 with NVIDIA NGC MXNet Container Pre-release by NVIDIA. Single Node: Resnet-50 V1 Training on NVIDIA DGX-2 with NVIDIA NGC MXNet Container by NVIDIA. At scale: Resnet-50 Training with 2048 P40 GPUs by Tencent. Translation: Single Node Strong NMT (Transformer) Training on WMT 14 English-German Translation with DGX-1 by Facebook Research. At Scale Strong NMT (Transformer) Training on WMT 14 English-German Translation with 16 DGX-1 Systems by Facebook Research.

13 MASTERING THE COMPLEX CHALLENGE OF INFERENCE IMAGES TRANSLATION Programmability: Increasing diversity of neural networks FASTEST INFERENCE HIGHEST INFERENCE THROUGHPUT HIGHEST INFERENCE EFFICIENCY HIGHEST INFERENCE THROUGHPUT Latency: The right answer, right now Accuracy: If the answer isn t right, speed doesn t matter 1 millisecond 6,250 images/second 56 images/second/watt 13,160 words/second Size of network: Exploding neural network complexity CAMBRIAN EXPLOSION OF NETWORKS 12,000+ NVIDIA TENSORRT DOWNLOADS FROM 4,000 COMPANIES Throughput: Concurrently run many AI models Convolutional Networks Recurrent Networks NLP Image Maps Efficiency: Deliver performance within power budget Search Video Rate of learning: Constant retraining and deployment, sometimes multiple times per day Generative Adversarial Networks Reinforcement Learning Speech Images: Fastest Inference: Resnet-50 Inference on Tesla V100 with TensorRT, Batch size: 1, Int8 optimized. Inference Throughput: Resnet-50 Inference on Tesla V100 with TensorRT, Batch size: 128, Int8 Optimized. Inference Efficiency: Resnet-50 Inference on Tesla T4, Int8 optimized, batch size = 32. Translation: GMNT Inference on Newstest2015 test dataset on Tesla V100.

14 ResNet-50 Inference (i/s) TENSORRT HYPERSCALE PLATFORM BOOSTS SERVER UTILIZATION NV DL SDK TENSORRT HYPERSCALE DNN Models GIANT LEAPS IN INFERENCE PERF. 20,000 15,000 Turing INT4 Tesla T4 is a universal inference accelerator designed for hyperscale datacenters. TensorRT Hyperscale is a fully optimized Kubernetes stack and an inference server that enables multiple types of models from different frameworks to run concurrently on one GPU node. TensorRT Hyperscale enables higher utilization, scalability, and resilience for large-scale deep learning operations. NV Docker 21X ASR Deep Speech 2 8X TTS WaveNet 36X NLP GNMT TensorRT Inference Server Kubernetes 20X RECOM NCF 27X VIDEO/IMAGE ResNet-50 10, ,000 0 Pascal Kepler Maxwell FP32 FP32 FP TENSOR CORE OPS 16X PASCAL FP INFERENCE FLOAT INT8 INT4 P4 Volta FP16 Turing INT FLOAT INT8 INT4 T4 FLOAT INT8 INT4 FLOAT INT8 INT4

15 AI IS REVOLUTIONIZING THE ENTERPRISE Every industry is tapping into the power of AI; our full-system expertise enables enterprise adoption. DGX-2: World s most powerful AI system for datacenter deployments; $399K system can replace $3M of CPU servers. $20B TAM IN 2023 INDUSTRY LEADER ADOPTION Transportation Healthcare Public Sector Manufacturing Oil & Gas Cloud/Other HGX-2: Cloud server platform for OEM and ODM partners; Single HGX-2 can replace 300 dual-cpu server nodes. DGX FOR DATACENTERS HGX FOR CLOUD

16 ACCELERATED DATACENTER VALUE THE MORE YOU BUY, THE MORE YOU SAVE WORKLOAD BASELINE CPU-Only HPC (Amber, LAMMPS, NAMD, VASP) VISUALIZATION (Rendering, Ray Tracing) AI TRAINING (Caffe2, TensorFlow, MXNet) AI INFERENCE (Image, Speech, Translation) Speed Up 1X 20X 60X >100X 200X Servers 5, <50 25 Capex $45M $11M $10.5M $7.5M $1.5M 3-Year Opex (Power + Cooling) $19.5M $2.5M $2M $1M $0.5M TCO Savings N/A 79% 81% 86% 97% Note(s): CPU Baselined to 5000 Servers for each workload Capex Costs: CPU node with 2x Skylake CPU s ~$9K; Opex Costs: Power & cooling is $180/kW/month Power: CPU server + n/w = 0.6 KW; GPU server + n/w = 1.6 KW; DGX-1V/HGX-1 Server = 3.2KW Visualization: GPU node with 8x RTX 8000 GPU s compared to 12 core CPU server HPC: GPU node with 4xV100 compared to 2xCPU Server DL Training: DGX-1V compared to a 2xCPU server DL Inference: GPU Node with 16 xt4 Compared to 2x CPU Server numbers rounded to nearest $0.5M

17 EXAMPLE AI COMPUTATION PIPELINE INPUT DATA PROCESSING PERCEPTION & INFERENCE REASONING ACTION Where can I go for ramen? Calibrate sensor Decompression Tone mapping De-warping ICP FFT/IFFT De-noise Optical flow Motion comp Feature point Beam-forming Iterative recon Odometry Tracking SLAM Deep Learning (CNN, RNN, GAN, U-Net, MLP, etc.) 3D-2D transform Image registration Classification Route/lane/path planning Env prediction A*/D* Collision testing Language translation Tree search Trajectory planning Vehicle dynamics LQR F/I kinematics Visualization Compression Let me suggest Jankara Ramen ISP Video Processor Vision Process CUDA CPU Tensor Core CUDA CPU Tensor Core CUDA CPU CUDA GPU Video Processor CPU

18 AGX FOR AUTONOMOUS MACHINES AND AI IOT Xavier, the first chip ever designed for autonomous AI systems, will enable automation in many large industries. AGX is our line of systems that addresses this opportunity. DRIVE AGX for autonomous cars, trucks, and robotaxis. DRIVE JETSON CLARA Jetson AGX for robots and drones, as well as AI IOT systems for safe and smart cities. Clara AGX for next-generation imaging in medical instruments. For each, we ve created a full stack to enable market leaders to take advantage of our platform. NVIDIA AGX

19 NVIDIA DRIVE FOR AV REVOLUTION DRIVING Cars Pedestrians Lanes Path Signs Lights DRIVE is a full-stack open platform for the transportation industry. More than a driving computer and software stack, DRIVE is an HPC infrastructure for training, simulation, and testing. DRIVE AGX car computer delivers the performance needed to run a wide array of deep learning models and algorithms concurrently. TRAINING SIMULATING Companies can build their own AV applications with the new DRIVE AGX DevKit.

20 NVIDIA DRIVE ADOPTION ACROSS $10T TRANSPORTATION INDUSTRY Hundreds of automakers, mobility services, truck makers, tier ones, mapping, and sensor companies are developing on DRIVE to create the future of transportation. Toyota is using DRIVE AGX Xavier as the AI brain in production cars beginning Daimler and Bosch are developing robotaxi fleets with DRIVE. VW is developing AI-infused cockpit experiences on DRIVE. $25B TAM opportunity by 2025.

21 JETSON AGX AND ISAAC DELIVER AI TO $250B ROBOTICS AND IOT INDUSTRY The next chapter of AI is autonomous machines. Jetson AGX Xavier delivers the energyefficient computational power needed for embedded systems like robots, drones, and smart cities. NVIDIA Isaac is a simulation environment and SDK that accelerates the development and deployment of robots of all kinds. Jetson AGX Xavier DevKit is available for order now. $5B TAM opportunity by JETSON AGX XAVIER DEV KIT NVIDIA ISAAC

22 JETSON AGX TRANSFORMS $100B MEDICAL IMAGING INDUSTRY Ultrasound, CT, MR, Endoscopy, Sequencing, and Pathology allow us to see the invisible in the human body. Instruments are large sensors that require supercomputing level of processing for high-speed sensor processing, reconstruction, AI, and visualization. Traditionally build with FPGA, CPU, and GPU. Clara AGX Xavier replaces all three into one programmable architecture. $2B TAM opportunity by CLARA AGX AI-ENHANCED ULTRASOUND Scale Up to 200 TOPS DL Processing 8 GIGA Rays 200W 2D Visualization 3D Segmented Visualization

23 Large markets Gaming, Pro Viz, HPC, AI, Transportation, Healthcare, Robotics, and AI IOT adopt NVIDIA Accelerated Computing as Moore s law ends. INCREASING NVIDIA ADOPTION NEW PRODUCTS NEW MARKETS ANOTHER GIANT LEAP 1M+ developers 56% of new FLOPS in Top500 12,000+ TensorRT downloads from 4,000 companies New Turing is a giant leap for GPU extending leadership in gaming and opening new growth for rendering and hyperscale AI. New AGX line fuses HPC and AI opening new growth opportunities in AV, medical instruments, robotics, and AI IOT. Leveraging one architecture across multiple large markets allows us to drive growth and margin expansion.

24 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES

25 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES GROSS MARGIN ($ in millions & margin percentage) NON-GAAP STOCK-BASED COMPENSATION (A) PRODUCT WARRANTY (B) GAAP $2,846 (15) (20) $2,811 FY % (0.3) (0.4) 56.1% $4,059 (16) $4,043 1H FY % (0.2) 63.9% 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

26 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.) OPERATING MARGIN ($ in millions & margin percentage) NON-GAAP STOCK-BASED COMPENSATION (A) RESTRUCTURING COSTS OTHER (B) GAAP $1,125 (205) (131) (42) $747 FY % (4.1) (2.6) (0.9) 14.9% $2,718 (262) (4) $2,452 1H FY % (4.2) 38.7% 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, amortization of acquisition-related intangible assets, transaction costs, compensation charges, and other credits related to acquisitions.

27 RECONCILIATION OF NON-GAAP TO GAAP FINANCIAL MEASURES (CONTD.) ($ IN MILLIONS) FY16 Q2 FY19 GAAP net cash flow provided by operating activities $1,175 $913 Purchase of property and equipment and intangible assets (86) (128) Free cash flow $1,089 $785

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