Nvidia investor relations Oct 2024

Nvidia investor relations Oct 2024

• 1. Company Overview May 23, 2024

• 2. Except for the historical information contained herein, certain matters in this presentation including, but not limited to, statements as to: our financial position; our markets, market opportunity, demand and growth drivers; our financial outlook; the benefits, impact, performance, features and availability of our products and technologies; the benefits, impact, features and timing of our collaborations or partnerships; third parties adopting our products and technologies; NVIDIA accelerated computing being broadly recognized as the way to advance computing as Moore’s law ends and AI lifts off; accelerated computing being needed to tackle the most impactful opportunities of our time; AI driving a platform shift from general purpose to accelerated computing, and enabling new, never-before-possible applications; trillion dollars of installed global data center infrastructure transitioning to accelerated computing; broader enterprise adoption of AI and accelerated computing under way; AI and accelerated computing making possible the next big waves of autonomous machines and industrial digitalization; a rapidly growing universe of applications and industry innovation; the ability of developers to engage with NVIDIA through CUDA; AI augmenting creativity and productivity by orders of magnitude across industries ; generative AI as the most important computing platform of our generation; data centers becoming AI factories; large language models being one of today’s most important advanced AI technologies, involving up to trillions of parameters that learn from text; full-stack and data center scale acceleration driving significant cost savings and workload scaling; the high ROI of high compute performance; NVIDIA powering the AI industrial revolution; the ability of developers to connect additional or third party services to the AI chatbot via cloud AI APIs; AI factories acting as trusted engines of generative AI; features of AI factories; nations using AI factories as sovereign national resources to process private datasets of companies, startups, universities and governments safely on shore to produce valuable insights; every important company running its own AI factories; NVIDIA generating recurring revenue from AI factories for their use of NVIDIA AI Enterprise, the operating system for enterprise AI, in addition to the up-front revenue opportunity from data center systems; our dividend program plan; our strategic investments; NVIDIA on track to achieve 100% renewable electricity for offices and data centers under operational control by end of FY25; and NVIDIA’s plan to engage manufacturing suppliers comprising at least 67% of scope 3 category 1 GHG emissions to effect supplier adoption of science-based targets by end of FY26 are forward-looking statements. 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 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. NVIDIA has based these forward-looking statements largely on its current expectations and projections about future events and trends that it believes may affect its financial condition, results of operations, business strategy, short-term and long-term business operations and objectives, and financial needs. These forward-looking statements are subject to a number of risks and uncertainties, and you should not rely upon the forward-looking statements as predictions of future events. The future events and trends discussed in this presentation may not occur and actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. Although NVIDIA believes that the expectations reflected in the forward-looking statements are reasonable, the company cannot guarantee that future results, levels of activity, performance, achievements or events and circumstances reflected in the forward-looking statements will occur. Except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. 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 most recent Annual Report on Form 10-K, Quarterly Reports on Form 10-Q, and Current Reports on Form 8-K. Copies of reports we file with the SEC are posted on our website and are available from NVIDIA without charge. Many of the products and features described herein remain in various stages and will be offered on a when-and-if-available basis. The statements within are not intended to be, and should not be interpreted as a commitment, promise, or legal obligation, and the development, release, and timing of any features or functionalities described for our products is subject to change and remains at the sole discretion of NVIDIA. NVIDIA will have no liability for failure to deliver or delay in the delivery of any of the products, features or functions set forth herein. NVIDIA uses certain non-GAAP measures in this presentation including non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating income, non-GAAP operating margin, and free cash flow. NVIDIA believes the presentation of its non-GAAP financial measures enhances investors' overall understanding of the company's historical financial performance. The presentation of the company's non-GAAP financial measures is not meant to be considered in isolation or as a substitute for the company's financial results prepared in accordance with GAAP, and the company's non-GAAP measures may be different from nonGAAP measures used by other companies. Further information relevant to the interpretation of non-GAAP financial measures, and reconciliations of these non-GAAP financial measures to the most comparable GAAP measures, may be found in the slide titled “Reconciliation of Non-GAAP to GAAP Financial Measures.”

• 3. Headquarters: Santa Clara, CA NVIDIA pioneered accelerated computing to help solve impactful challenges classical computers cannot. A quarter of a century in the making, NVIDIA accelerated computing is broadly recognized as the way to advance computing as Moore’s law ends and AI lifts off. NVIDIA’s platform is installed in several hundred million computers, is available in every cloud and from every server maker, powers over 76% of the TOP500 supercomputers, and has over 5 million developers. Headquarters: Santa Clara, CA Headcount: ~29,600

• 4. NVIDIA’s Accelerated Computing Platform Full-stack innovation across silicon, systems and software With nearly three decades of singular focus, NVIDIA is expert at accelerating software and scaling compute by a Million-X, going well beyond Moore’s law Accelerated computing requires full-stack innovation—optimizing across every layer of computing—from silicon and systems to software and algorithms, demanding deep understanding of the problem domain Our full-stack platforms—NVIDIA AI and NVIDIA Omniverse—accelerate AI and industrial digitalization workloads We accelerate workloads at data center scale, across thousands of compute nodes, treating the network and storage as part of the computing fabric Our platform extends from the cloud and enterprise data centers to supercomputing centers, edge computing and PCs Magnum IO DOCA Base Command Forge APPLICATION FRAMEWORKS HARDWARE ACCELERATION LIBRARIES PLATFORM NVIDIA AI NVIDIA OMNIVERSE SYSTEM SOFTWARE RTX DGX HGX EGX OVX AGX MLNX GPU CPU DPU NIC SWITCH SOC CUDA-X CUDA RTX

• 5. What Is Accelerated Computing? Not just a superfast chip—accelerated computing is a full-stack combination of: • Chip(s) with specialized processors • Algorithms in acceleration libraries • Domain experts to refactor applications To speed-up compute-intensive parts of an application A full-stack approach: silicon, systems, software For example: • If 90% of the runtime can be accelerated by 100X, the application is sped up 9X • If 99% of the runtime can be accelerated by 100X, the application is sped up 50X • If 80% of the runtime can be accelerated by 500X, or even 1000X, the application is sped up 5X Amdahl’s law: The overall system speed-up (S) gained by optimizing a single part of a system by a factor (s) is limited by the proportion of execution time of that part (p). 𝑆 = 1 1 − 𝑝 + 𝑝 𝑠

• 6. Why Accelerated Computing? Accelerated computing is needed to tackle the most impactful opportunities of our time—like AI, climate simulation, drug discovery, ray tracing, and robotics NVIDIA is uniquely dedicated to accelerated computing —working top-to-bottom, refactoring applications and creating new algorithms, and bottom-to-top—inventing new specialized processors, like RT Core and Tensor Core Advancing computing in the post-Moore’s Law era 102 10 3 104 10 5 106 10 7 109 10 8 1980 1990 2000 2010 2020 2030 GPU-Computing perf 2X per year 1000X In 10 years Single-threaded CPU perf 1.5X perf per year 1.1X per year Trillions of Operations per Second (TOPS) “It’s the end of Moore’s Law as we know it.” —John Hennessy Oct 23, 2018 “Moore’s Law is dead.” —Jensen Huang, GTC 2013

• 7. 2016 2017 2020 2022 2024 TFLOPS Pascal 19 TFLOPS FP16 Volta 130 TFLOPS FP16 Ampere 620 TFLOPS BF16/FP16 Blackwell 20,000 TFLOPS FP4 Hopper 4,000 TFLOPS FP8 1000x AI Compute in 8 Years

• 8. A new computing era has begun Accelerated computing enabled the rise of AI, which is driving a platform shift from general purpose to accelerated computing, and enabling new, never-before-possible applications The trillion dollars of installed global data center infrastructure will transition to accelerated computing to achieve better performance, energy-efficiency and cost by an order of magnitude Hyperscale cloud service providers and consumer internet companies have been the early adopters of AI and accelerated computing, with broader enterprise adoption now under way AI and accelerated computing will also make possible the next big waves—autonomous machines and industrial digitalization Waves of Adoption of Accelerated Computing A generational computing platform shift Cloud Service Providers & Consumer Internet Enterprise Autonomous Vehicles & Robotics Industrial Digitalization

• 9. NVIDIA Accelerated Computing for Every Wave NVIDIA HGX is an AI supercomputing platform purpose-built for AI. It includes 8 NVIDIA GPUs, as well as interconnect and networking technologies, delivering order-of-magnitude performance speed-ups for AI over CPU servers. It is broadly available from all major server OEMs/ODMs. NVIDIA DGX, an AI server based on the same architecture, along with NVIDIA AI software and support, is also available. NVIDIA Omniverse is a software platform for designing, building, and operating 3D and virtual world simulations. It harnesses the power of NVIDIA graphics and AI technologies and runs on NVIDIA-powered data centers and workstations. NVIDIA DRIVE is a full-stack platform for autonomous vehicles (AV) that includes hardware for in-car compute, such as the Orin system-on-chip, and the full AV and AI cockpit software stack. NVIDIA AI Enterprise is the operating system of AI, with enterprise-grade security, stability, manageability and support. It is available on all major CSPs and server OEMs and supports enterprise deployment of AI in production. NVIDIA DGX Cloud is a cloud service that allows enterprises immediate access to the infrastructure and software needed to train advanced models for generative AI and other groundbreaking applications. Cloud Service Providers & Consumer Internet Enterprise Autonomous Vehicles & Robotics Industrial Digitalization

• 10. NVIDIA’s Accelerated Computing Ecosystem • The NVIDIA accelerated computing platform has attracted the largest ecosystem of developers, supporting a rapidly growing universe of applications and industry innovation • Developers can engage with NVIDIA through CUDA—our parallel computing programming model introduced in 2006—or at higher layers of the stack, including libraries, pre-trained AI models, SDKs and other development tools Developers CUDA Downloads* AI Startups GPU-Accelerated Applications 2021 2024 7K 19K 2021 2024 5.1M 2.5M 2021 2024 53M 26M 2021 2024 3,700 1700 *Cumulative

• 11. The virtuous cycle of NVIDIA’s accelerated computing starts with an installed base of several hundred million GPUs, all compatible with the CUDA programming model • For developers—NVIDIA’s one architecture and large installed base give developer’s software the best performance and greatest reach • For end users—NVIDIA is offered by virtually every computing provider and accelerates the most impactful applications from cloud to edge • For cloud providers and OEMs—NVIDIA’s rich suite of Acceleration Platforms lets partners build one offering to address large markets including media & entertainment, healthcare, transportation, energy, financial services, manufacturing, retail, and more • For NVIDIA—Deep engagement with developers, computing providers, and customers in diverse industries enables unmatched expertise, scale, and speed of innovation across the entire accelerated computing stack — propelling the flywheel NVIDIA’s Multi-Sided Platform and Flywheel NVIDIA Accelerated Computing Virtuous Cycle Developers Cloud & OEMs Installed Base Scale End-Users Speed-Up R&D $

• 12. Office AI Copilots Search & Social Media Over 1B knowledge workers $700B in digital advertising annually AI Content Creation 50M creators globally Legal Services, Education 1M legal professionals in the US 9M educators in the US AI Software Development Financial Services 30M software developers globally 678B annual credit card transactions Customer Service with AI 15M call center agents globally Drug Discovery 1018 molecules in chemical space 40 exabytes of genome data Agri-Food | Climate 1B people in agri-food worldwide Earth-2 for km-scale simulation Knowledge workers will use copilots based on large language models to generate documents, answer questions, or summarize missed meetings, emails and chats—adding hours of productivity per week Copilots specialized for fields such as software development, legal services or education can boost productivity by as much as 50% Social media, search and e-commerce apps are using deep recommenders to offer more relevant content and ads to their customers, increasing engagement and monetization Creators can generate stunning, photorealistic images with a single text prompt—compressing workflows that take days or weeks into minutes in industries from advertising to game development Call center agents augmented with AI chatbots can dramatically increase productivity and customer satisfaction Drug discovery, financial services, agriculture and food services and climate forecasting are seeing order-of-magnitude workflow acceleration from AI Huge ROI From AI Driving a Powerful New Investment Cycle AI can augment creativity and productivity by orders of magnitude across industries Source: Goldman Sachs, Cowen, Statista, Capital One, Wall Street Journal, Resource Watch, NVIDIA internal analysis

• 13. Generative AI The most important computing platform of our generation The era of generative AI has arrived, unlocking new opportunities for AI across many different applications Generative AI is trained on large amounts of data to find patterns and relationships, learning the representation of almost anything with structure It can then be prompted to generate text, images, video, code, or even proteins For the very first time, computers can augment the human ability to generate information and create 1,600+ Generative AI companies are building on NVIDIA TEXT SOUND TEXT TEXT IMAGE VIDEO SPEECH MULTI-MODAL AMINO ACID BRAINWAVES SPEECH IMAGE VIDEO IMAGE 3D ANIMATION MANIPULATION PROTEIN Learn and Understand Everything

• 14. Large Language Models, based on the Transformer architecture, are one of today’s most important advanced AI technologies, involving up to trillions of parameters that learn from text. Developing them is an expensive, time-consuming process that demands deep technical expertise, distributed data center-scale infrastructure, and a full-stack accelerated computing approach. Modern AI Is a Data Center Scale Computing Workload Data centers are becoming AI factories: Data as input, intelligence as output AI Training Computational Requirements Fueling Giant-Scale AI Infrastructure NVIDIA compute & networking GPU | DPU | Switch | CPU AlexNet VGG-19 Seq2Seq Resnet InceptionV3 Xception ResNeXt DenseNet201 ELMo MoCo ResNet50 Wav2Vec 2.0 Transformer GPT-1 BERT Large GPT-2 1.5B XLNet Megatron-NLG Microsoft T-NLG GPT3-175B MT NLG 530B BLOOM Chinchilla PaLM GPT-MoE-1.8T 2012 2014 2016 2018 2020 2022 2024 Training Compute (petaFLOPs) Training Compute PFLOPs 102 103 104 105 106 107 108 109 1010

• 15. Full-Stack & Data Center Scale Acceleration Drive significant cost savings and workload scaling Classical Computing—960 CPU-only servers Accelerated Computing—2 GPU servers 25X lower cost 84X better energy-efficiency Application Application Re-Engineered for Acceleration Magnum IO CPU server racks CUDA-X Acceleration Libraries LLM Workload: Bert-Large Training and Inference | CPU Server: Dual-EYPC 7763 | GPU Server: Dual-EPYC 7763 + 8X H100 PCIe GPUs

• 16. The High ROI of High Compute Performance Rental Cost 4-Year Cost of AI Infrastructure ~$1B 16K GPU 4-Year Rental Opportunity @$4 per GPU-HR ~$2.5B GPU Compute Networking DC Facility Build & Operate 25% Performance Increase Worth $600M+ 15% Utilization Increase Worth $350M+ $1 upfront investment in NVIDIA compute and networking can translate to $5 in CSP revenue over 4 years Illustrative example of NVIDIA GPU cost vs AI infrastructure total cost of ownership (TCO)

• 17. Powering the AI Industrial Revolution Building and running enterprise Gen AI applications Enterprise AI Chatbot with “RAG” NVIDIA AI foundry service for building Enterprise AI applications NVIDIA AI enterprise ecosystem for running Enterprise AI applications Enterprise AI chatbots Are built with Retrieval Augmented Generation (RAG), which augments the knowledge in the LLM with Enterprise data mapped to a Vector Database, thus reducing “hallucinations”. Developers can connect additional or 3rd party services to the AI chatbot via cloud AI APIs. Vector Database NIM NVIDIA AI Enterprise Cloud AI APIs AI Foundation Model Tech DGX Cloud Factory NVPS Experts NVIDIA DGX Cloud NVIDIA AI Foundation Pre-Trained LLMs Enterprise SaaS & AI Platforms Enterprise On-Prem Cloud DGX Cloud

• 18. The NVIDIA AI Foundry Model on DGX Cloud NVIDIA’s “AI foundry” service leverages our AI infrastructure and expertise to build custom AI models for enterprise customers— analogous to a semiconductor foundry that uses its infrastructure and expertise to build custom chips for fabless customers. An enterprise customer starts with an NVIDIA or 3rd party pre-trained AI model, available in NVIDIA AI Foundations. This model making service includes frameworks such as NVIDIA NeMo for custom LLMs and NVIDIA Picasso for custom generative AI for visual design. With help from NVIDIA experts, the enterprise customer fine-tunes the model on their proprietary enterprise data and adds guardrails, using tools available in NVIDIA AI Foundations. The fine-tuning and optimization is done on NVIDIA DGX Cloud, a cloud service that allows enterprises immediate access to NVIDIA AI infrastructure and software, hosted at partner cloud providers. The enterprise customer ends up with a fully-trained and optimized AI model, fine-tuned on their proprietary enterprise data, that can be deployed anywhere—in the cloud or on-prem. The NVIDIA AI Foundry model generates revenue based on per-node, per-month consumption of NVIDIA DGX Cloud. For building enterprise AI applications NVIDIA AI Foundations NeMo | Picasso Pre-trained LLMs NVIDIA DGX Cloud NVIDIA AI Foundry

• 19. AI Factories—A New Class of Data Centers ‘AI factories’ are next-generation data centers that host advanced, full-stack accelerated computing platforms for the most computationally intensive tasks, where data comes in and intelligence comes out. These new data centers will act as trusted engines of generative AI. Every important company will run its own AI factories to securely process its valuable proprietary data and turn it into monetizable tokens, encapsulating its knowledge, intelligence, and creativity. Nations are using AI factories as sovereign national resources— processing private datasets of companies, startups, universities and governments safely on shore to produce valuable insights. In addition to the up-front revenue opportunity from data center systems, NVIDIA can generate recurring revenue from AI factories with NVIDIA AI Enterprise, the operating system for enterprise AI. For running enterprise AI applications DATA TOKENS Enterprise SaaS & AI Platforms Enterprise On-Prem Cloud AI Factory NVIDIA AI Enterprise

• 20. NVIDIA AI Enterprise The operating system for enterprise AI NVIDIA AI Enterprise is software for deploying and running AI with enterprise-grade security, API stability, manageability and support. Cloud-native and available in every major cloud marketplace. Certified to run on servers and workstations from all major OEMs. Supported by all major global system integrators. Integrated with and distributed by VMware. NVIDIA AI Enterprise AI Use Cases and Workflows LLM Speech AI Recommenders Cybersecurity Medical Imaging Video Analytics Route Optimization More … Consumption pricing per GPU-hour Subscription pricing per GPU/year (included with H100 PCIe/DGX) NVIDIA AI Enterprise NVIDIA Certified Server Dell | HPE | Lenovo Azure | GCP | OCI | AWS Run Anywhere Cloud

• 21. GSI & Service Delivery AI Platforms Software Platforms Public Cloud Marketplaces Server OEMs NVIDIA AI Enterprise Broad and deep ecosystem and distribution to reach every enterprise Private Cloud

• 22. NVIDIA Inference Microservice (NIM) Extending reach of the platform, connecting millions of developers to hundreds of millions of CUDA GPUs in the installed base Cloud Native Stack GPU Operator, Network Operator Triton Inference Server cuDF, CV-CUDA, DALI, NCCL, Post Processing Decoder Enterprise Management Health Check, Identity, Metrics, Monitoring, Secrets Management Kubernetes Industry Standard APIs Text, Speech, Image, Video, 3D, Biology Customization Cache P-Tuning, LORA, Model Weights Optimized Model Single GPU, Multi-GPU, Multi-Node TensorRT LLM and Triton cuBLAS , cuDNN, In-Flight Batching, Memory Optimization, FP8 Quantization NVIDIA CUDA Available Now as Part of NVIDIA AI Enterprise 5.0 $4,500/GPU/YEAR, $1/GPU/HOUR

• 23. NVIDIA Go-to-Market Across Cloud and On-Premises Reaching customers everywhere INFERENCE Cloud On-Prem HGX MGX AGX IGX DGX NVIDIA AI Foundations - Cloud services for customizing and operating generative AI models DGX Cloud Partners

• 24. $3,735 $6,803 $12,690 $9,040 $37,134 $18,059 34% 41% 47% 34% 61% 69% 30% 40% 50% 60% 70% $0 $9,000 $18,000 $27,000 $36,000 $45,000 FY20 FY21 FY22 FY23 FY24 YTD FY25 Operating Income (Non-GAAP, $M) Operating Margin (Non-GAAP) $10,918 $16,675 $26,914 $26,974 $26,044 $60,922 FY20 FY21 FY22 FY23 FY24 YTD FY25 49% 36% 6% 3% 6% 10% 87% 2% Gaming Data Center ProViz Auto OEM & Other Driving Strong & Profitable Growth Revenue ($M) Fiscal year ends in January. Refer to Appendix for reconciliation of Non-GAAP measures. Operating margins rounded to the nearest percent. Q1 FY22 Q1 FY25

• 25. $6,821 $10,947 $17,969 $15,965 $44,959 $20,560 63% 66% 67% 59% 74% 79% 55% 60% 65% 70% 75% 80% 85% 90% $0 $6,000 $12,000 $18,000 $24,000 $30,000 $36,000 $42,000 $48,000 FY20 FY21 FY22 FY23 FY24 YTD FY25 Gross Profit (Non-GAAP, $M) Gross Margin (Non-GAAP) Cost comparison example based on latest available NVIDIA A100 GPU and Intel CPU inference results in the commercially available category of the MLPerf industry benchmark; includes related infrastructure costs such as networking. NVIDIA Gross Margins Reflect Value of Acceleration Fiscal year ends in January. Refer to Appendix for reconciliation of Non-GAAP measures. Gross margins are rounded to the nearest percent. Accelerated computing requires full-stack and data center-scale innovation across silicon, systems, algorithms and applications. Significant expertise and effort are required, but application speed-ups can be incredible, resulting in dramatic cost and time-to-solution savings. • For example, 2 NVIDIA HGX nodes with 16 NVIDIA H100 GPUs that cost $400K can replace 960 nodes of CPU servers that cost $10M for the same LLM workload. NVIDIA chips carry the value of the full-stack, not just the chip.

• 26. Strong Cash Flow Generation Fiscal year ends in January. Refer to Appendix for reconciliation of Non-GAAP measures. Share Repurchase Utilized cash towards $9.5B of repurchases in FY24 $14.5B Remaining Authorization as of end of Q1 Dividend $395M in FY24 Plan to Maintain 1 Strategic Investments Growing Our Talent Platform Reach & Ecosystem Free Cash Flow (Non-GAAP) Capital Allocation 1 Subject to continuing determination by our Board of Directors. $4.3B $4.7B $8.0B $3.8B $26.9B $14.9B 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 FY20 FY21 FY22 FY23 FY24 YTD FY25

• 27. DGX/HGX/MGX/IGX systems GPU | CPU | DPU | Networking NVIDIA AI software Our Market Platforms at a Glance FY24 Revenue $47.5B 5-YR CAGR 75% FY24 Revenue $10.4B 5-YR CAGR 11% FY24 Revenue $1.6B 5-YR CAGR 7% FY24 Revenue $1.1B 5-YR CAGR 11% GeForce GPUs for PC gaming GeForce NOW cloud gaming DRIVE Hyperion sensor architecture with AGX compute DRIVE AV & IX full stack software for ADAS, AV & AI cockpit NVIDIA RTX GPUs for workstations Omniverse software Professional Visualization Automotive 17% of FY24 Revenue 3% of FY24 Revenue 2% of FY24 Revenue Data Center 78% of FY24 Revenue Gaming

• 28. $2,983 $6,696 $10,613 $15,005 $47,525 $22,563 FY20 FY21 FY22 FY23 FY24 YTD FY25 Data Center The leading accelerated computing platform Leader in AI & HPC #1 in AI training and inference Used by all hyperscale and major cloud computing providers and over 40,000 companies Powers over 75% of the TOP500 supercomputers Growth Drivers Broad data center platform transition from general-purpose to accelerated computing Emergence of “AI factory” — optimized for refining data and training, inferencing, and generating AI Broader and faster product launch cadence to meet a growing and diverse set of AI opportunities DGX Cloud services and NVIDIA AI Enterprise software for building and running enterprise AI applications 75% 5-YR CAGR Through FY24 Revenue ($M)

• 29. NVIDIA Blackwell Platform GB200 Superchip Compute Node NVLINK Switch Quantum X800 Switch Spectrum X800 Switch BlueField-3 SuperNIC ConnectX-8 SuperNIC HGX B100

• 30. $5,518 $7,759 $12,462 $9,067 $10,447 $2,647 FY20 FY21 FY22 FY23 FY24 YTD FY25 Gaming GeForce—the world’s largest gaming platform Leader in PC Gaming Strong #1 market position 15 of the top 15 most popular GPUs on Steam Leading performance & innovation 200M+ gamers on GeForce Growth Drivers Rising adoption of NVIDIA RTX in games Expanding universe of gamers & creators Gaming laptops & Gen AI on PCs GeForce NOW Cloud gaming 11% 5-YR CAGR Through FY24 Revenue ($M)

• 31. GeForce Extends Growth, Large Upgrade Opportunity Installed Base Needs Upgrade FY21 FY24 3YR CAGR ASP 16% Units -2% 53% RTX 25% RTX3060+ Performance $699+ Cumulative Sell-Through $ 13% CAGR 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 NVIDIA Ampere NVIDIA Ada Weeks After Launch NVIDIA Turing Source: NVIDIA estimates GeForce Gaming Revenue Installed Base RTX 3060+

• 32. $1,212 $1,053 $2,111 $1,544 $1,553 $427 FY20 FY21 FY22 FY23 FY24 YTD FY25 Professional Visualization Workstation graphics Leader in Workstation Graphics 95%+ market share in graphics for workstations 45M Designers and Creators Strong software ecosystem with over 100 RTX accelerated and supported applications Growth Drivers Gen AI adoption across design and creative industries Enterprise AI development, model fine tuning, cross-industry Ray tracing revolutionizing design and content creation Expanding universe of designers and creators Omniverse for digital twins and collaborative 3D design Hybrid work environments 7% 5-YR CAGR Through FY24 Revenue ($M)

• 33. Automotive Autonomous vehicle and AI cockpit Leader in Autonomous Driving NVIDIA DRIVE is our end-to-end Autonomous Vehicle (AV) and AI Cockpit platform featuring a full software stack and is powered by NVIDIA (systems-on-a-chip) SoCs in the vehicle DRIVE Orin SoC ramp began in FY23 Next-generation DRIVE Thor SoC ramp to begin in FY26 Over 40 customers including 20 of top 30 EV makers, 7 of top 10 truck makers, 8 of top 10 robotaxi makers Growth Drivers Adoption of centralized car computing and software-defined vehicle architectures AV software and services: Mercedes-Benz Jaguar Land Rover Revenue ($M) 11% 5-YR CAGR Through FY24 $700 $536 $566 $903 $1,091 $329 FY20 FY21 FY22 FY23 FY24 YTD FY25

• 34. $1 Trillion Long-Term Available Market Opportunity Data Center Systems $300B Omniverse Enterprise $150B Autonomous Machines $300B NVIDIA AI Enterprise & DGX Cloud $150B Gaming $100B Cloud Service Providers & Consumer Internet Enterprise Autonomous Vehicles & Robotics Industrial Digitalization

• 35. Financials

• 36. Annual Cash & Cash Flow Metrics 4,761 5,822 9,108 5,641 28,090 FY20 FY21 FY22 FY23 FY24 3,735 6,803 12,690 9,040 37,134 FY20 FY21 FY22 FY23 FY24 4,272 4,677 8,049 3,750 26,947 FY20 FY21 FY22 FY23 FY24 10,897 11,561 21,208 13,296 25,984 FY20 FY21 FY22 FY23 FY24 Cash balance is defined as cash and cash equivalents plus marketable securities Refer to Appendix for reconciliation of non-GAAP measures Free Cash Flow (Non-GAAP)—$M Cash Balance—$M Operating Income (Non-GAAP)—$M Operating Cash Flow—$M

• 37. Corporate Sustainability Fast Company Magazine’s World’s 50 Most Innovative Companies Fortune’s World’s Most Admired Companies Time Magazine’s 100 Most Influential Companies Wall Street Journal’s Management Top 250 “America’s Most Sustainable Companies” BARRON’S “America’s 100 Best Companies to Work For” FORTUNE “America’s Most Responsible Companies” NEWSWEEK NVIDIA Blackwell GPUs are as much as 20X more energy efficient than CPUs for certain AI and HPC workloads On track to achieve 100% renewable electricity for offices and data centers under operational control by end of FY25 Environmentally Conscious A Place For People To Do Their Life’s Work Plan to engage manufacturing suppliers comprising at least 67% of scope 3 category 1 GHG emissions to effect supplier adoption of science-based targets by end of FY26 Management 50% of Board is Gender, Racially, or Ethnically Diverse 92% of Directors are independent Corporate Governance “Best Places to Work” GLASSDOOR

• 38. Reconciliation of Non-GAAP to GAAP Financial Measures

• 39. Reconciliation of Non-GAAP to GAAP Financial Measures Gross Margin ($ in Millions & Margin Percentage) Non-GAAP Acquisition-Related and Other Costs (A) Stock-Based Compensation (B) Other (C) GAAP FY 2020 $6,821 — (39) (14) $6,768 62.5% — (0.4) (0.1) 62.0% FY 2021 $10,947 (425) (88) (38) $10,396 65.6% (2.6) (0.5) (0.2) 62.3% FY 2022 $17,969 (344) (141) (9) $17,475 66.8% (1.4) (0.5) — 64.9% FY 2023 $15,965 (455) (138) (16) $15,356 59.2% (1.7) (0.5) (0.1) 56.9% FY 2024 $44,959 (477) (141) (40) $44,301 73.8% (0.8) (0.2) (0.1) 72.7% YTD 2025 $20,560 (119) (36) 1 $20,406 78.9% (0.4) (0.1) — 78.4% A. Consists of amortization of intangible assets and inventory step-up B. Stock-based compensation charge was allocated to cost of goods sold C. Other consists of IP-related costs and assets held for sale related adjustments

• 40. Operating Income and Margin ($ in Millions & Margin Percentage) Non-GAAP Acquisition Termination Cost Acquisition-Related and Other Costs (A) Stock-Based Compensation (B) Other (C) GAAP FY 2020 $3,735 — (31) (844) (14) $2,846 34.2% — (0.3) (7.7) (0.1) 26.1% FY 2021 $6,803 — (836) (1,397) (38) $4,532 40.8% — (5.0) (8.4) (0.2) 27.2% FY 2022 $12,690 — (636) (2,004) (9) $10,041 47.2% — (2.5) (7.4) — 37.3% FY 2023 $9,040 (1,353) (674) (2,710) (79) $4,224 33.5% (5.0) (2.5) (10.0) (0.3) 15.7% FY 2024 $37,134 — (583) (3,549) (30) $32,972 61.0% — (1.0) (5.8) (0.1) 54.1% YTD 2025 $18,059 — (140) (1,011) 1 $16,909 69.3% — (0.5) (3.9) — 64.9% Reconciliation of Non-GAAP to GAAP Financial Measures (contd.) A. Consists of amortization of acquisition-related intangible assets, inventory step-up, transaction costs, compensation charges, and other costs B. Stock-based compensation charge was allocated to cost of goods sold, research and development expense, and sales, general and administrative expense C. Comprises of legal settlement cost, contributions, restructuring costs and assets held for sale related adjustments

• 41. ($ in Millions) Free Cash Flow Purchases Related to Property and Equipment and Intangible Assets Principal Payments on Property and Equipment and Intangible Assets Net Cash Provided by Operating Activities FY 2020 $4,272 489 — $4,761 FY 2021 $4,677 1,128 17 $5,822 FY 2022 $8,049 976 83 $9,108 FY 2023 $3,750 1,833 58 $5,641 FY 2024 $26,947 1,069 74 $28,090 YTD 2025 $14,936 369 40 $15,345 Reconciliation of Non-GAAP to GAAP Financial Measures (contd.)

• 42.