NVIDIA CORP Q1 2026 earnings call

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2026. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2026. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially.

For a discussion of factors that could affect our future financial results in business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 28th, 2025, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette.

Thank you, Toshiya. We delivered another strong quarter with revenue of $44 billion, up 69% year-over-year, exceeding our outlook in what proved to be a challenging operating environment. Data center revenue of $39 billion grew 73% year-on-year. AI workloads have transitioned strongly to inference, and AI factory buildouts are driving significant revenue. Our customers' commitments are firm. On April 9, the U.S. government issued new export controls on H20, our data center GPU designed specifically for the China market. We sold H20 with the approval of the previous administration. Although our H20 has been in the market for over a year and does not have a market outside of China, the new export controls on H20 did not provide a grace period to allow us to sell through our inventory.

In Q1, we recognized $4.6 billion in H20 revenue, which occurred prior to April 9th, but also recognized a $4.5 billion charge as we wrote down inventory and purchase obligations tied to orders we had received prior to April 9th. We were unable to ship $2.5 billion in H20 revenue in the first quarter due to the new export controls. The $4.5 billion charge was less than what we initially anticipated as we were able to reuse certain materials. We are still evaluating our limited options to supply data center compute products compliant with the U.S. government's revised export control rules. Losing access to the China AI accelerator market, which we believe will grow to nearly $50 billion, would have a material adverse impact on our business going forward and benefit our foreign competitors in China and worldwide.

Our Blackwell ramp, the fastest in our company's history, drove a 73% year-on-year increase in data center revenue. Blackwell contributed nearly 70% of data center compute revenue in the quarter, with a transition from Hopper nearly complete. The introduction of GB200 NVL was a fundamental architectural change to enable data center-scale workloads and to achieve the lowest cost per inference token. While these systems are complex to build, we have seen a significant improvement in manufacturing yields, and rack shipments are moving to strong rates to end customers. GB200 NVL racks are now generally available for model builders, enterprises, and sovereign customers to develop and deploy AI. On average, major hyperscalers are each deploying nearly 1,000 NVL72 racks or 72,000 Blackwell GPUs per week and are on track to further ramp output this quarter.

Microsoft, for example, has already deployed tens of thousands of Blackwell GPUs and is expected to ramp to hundreds of thousands of GB200s with OpenAI as one of its key customers. Key learnings from the GB200 ramp will allow for a smooth transition to the next phase of our product roadmap, Blackwell Ultra. Sampling of GB300 systems began earlier this month at the major CSPs, and we expect production shipments to commence later this quarter. GB300 will leverage the same architecture, same physical footprint, and the same electrical and mechanical specifications as GB200. The GB300 drop-in design will allow CSPs to seamlessly transition their systems and manufacturing used for GB200 while maintaining high yields. B300 GPUs with 50% more HBM will deliver another 50% increase in dense FP4 inference compute performance compared to the B200.

We remain committed to our annual product cadence, with our roadmap extending through 2028, tightly aligned with the multiple-year planning cycles of our customers. We are witnessing a sharp jump in inference demand. OpenAI, Microsoft, and Google are seeing a step-function leap in token generation. Microsoft processed over 100 trillion tokens in Q1, a fivefold increase on a year-over-year basis. This exponential growth in Azure OpenAI is representative of strong demand for Azure AI Foundry, as well as other AI services across Microsoft's platform. Inference-serving startups are now serving models using B200, tripling their token generation rate and corresponding revenues for high-value reasoning models such as DeepSeek R1, as reported by Artificial Analysis. NVIDIA Dynamo on Blackwell NVL72 turbocharges AI inference throughput by 30x for the new reasoning models sweeping the industry.

Developer engagements increased with adoption ranging from LLM providers such as Perplexity to financial services institutions such as Capital One, who reduced agentic chatbot latency by 5x with Dynamo. In the latest MLPerf inference results, we submitted our first results using GB200 NVL72, delivering up to 30x higher inference throughput compared to our eight GPU H200 submission on the challenging Llama 3.1 benchmark. This feat was achieved through a combination of tripling the performance per GPU as well as 9x more GPUs, all connected on a single NVLink domain. While Blackwell is still early in its life cycle, software optimizations have already improved its performance by 1.5x in the last month alone. We expect to continue improving the performance of Blackwell through its operational life, as we have done with Hopper and Ampere.

For example, we increased the inference performance of Hopper by four times over two years. This is the benefit of NVIDIA's programmable CUDA architecture and rich ecosystem. The pace and scale of AI factory deployments are accelerating with nearly 100 NVIDIA-powered AI factories in flight this quarter, a twofold increase year-over-year, with the average number of GPUs powering each factory also doubling in the same period. More AI factory projects are starting across industries and geographies. NVIDIA's full-stack architecture is underpinning AI factory deployments as industry leaders like AT&T, BYD, Capital One, Foxconn, MediaTek, and Telenor are strategically vital sovereign clouds like those recently announced in Saudi Arabia, Taiwan, and the UAE. We have a line of sight to projects requiring tens of GW of NVIDIA AI infrastructure in the not-too-distant future.

The transition from generative to agentic AI, AI capable of perceiving, reasoning, planning, and acting, will transform every industry, every company, and country. We envision AI agents as a new digital workforce capable of handling tasks ranging from customer service to complex decision-making processes. We introduced the Llama Nemotron family of open reasoning models designed to supercharge agentic AI platforms for enterprises. Built on the Llama architecture, these models are available as NIMs or NVIDIA Inference Microservices with multiple sizes to meet diverse deployment needs. Our post-training enhancements have yielded a 20% accuracy boost and a 5x increase in inference speed. Leading platform companies, including Accenture, Cadence, Deloitte, and Microsoft, are transforming work with our reasoning models. NVIDIA NeMo Microservices are generally available across industries and are being leveraged by leading enterprises to build, optimize, and scale AI applications.

With NeMo, Cisco increased model accuracy by 40% and improved response time by 10x in its code assistant. Nasdaq realized a 30% improvement in accuracy and response time in its AI platform's search capabilities. Shell's custom LLM achieved a 30% increase in accuracy when trained with NVIDIA NeMo. NeMo's parallelism techniques accelerated model training time by 20% when compared to other frameworks. We also announced a partnership with Yum Brands, the world's largest restaurant company, to bring NVIDIA AI to 500 of its restaurants this year and expanding to 61,000 restaurants over time to streamline order taking, optimize operations, and enhance service across its restaurants. For AI-powered cybersecurity, leading companies like Check Point, CrowdStrike, and Palo Alto Networks are using NVIDIA's AI security and software stack to build, optimize, and secure agentic workflows, with CrowdStrike realizing 2x faster detection triage with 50% less compute cost.

Moving to networking, sequential growth in networking resumed in Q1, with revenue up 64% quarter-over-quarter to $5 billion. Our customers continue to leverage our platform to efficiently scale up and scale out AI factory workloads. We created the world's fastest switch, NVLink. For scale-up, our NVLink compute fabric in its fifth generation offers 14x the bandwidth of PCIe Gen 5. NVLink 72 carries 130 TB per second of bandwidth in a single rack, equivalent to the entirety of the world's peak internet traffic. NVLink is a new growth vector and is off to a great start, with Q1 shipments exceeding $1 billion. At Computex, we announced NVLink Fusion. Hyperscale customers can now build semi-custom CCUs and accelerators that connect directly to the NVIDIA platform with NVLink.

We are now enabling key partners, including ASIC providers such as MediaTek, Marvell, Alchip Technologies, and Astera Labs, as well as CPU suppliers such as Fujitsu and Qualcomm, to leverage NVLink Fusion to connect our respective ecosystems. For scale-out, our enhanced Ethernet offerings deliver the highest throughput, lowest latency networking for AI. SpectrumX posted strong sequential and year-on-year growth and is now annualizing over $8 billion in revenue. Adoption is widespread across major CSPs and consumer internet companies, including CoreWeave, Microsoft Azure, Oracle Cloud, and xAI. This quarter, we added Google Cloud and Meta to the growing list of SpectrumX customers. We introduced SpectrumX and QuantumX silicon photonics switches featuring the world's most advanced co-package optics. These platforms will enable next-level AI factory scaling to millions of GPUs through the increasingly power efficiency by 3.5x and network resiliency by 10x while accelerating customer time to market by 1.3x.

Transitioning to a quick summary of our revenue by geography. China, as a percentage of our data center revenue, was slightly below our expectations and down sequentially due to H20 export licensing controls. For Q2, we expect a meaningful decrease in China data center revenue. As a reminder, while Singapore represented nearly 20% of our Q1 build revenue, as many of our large customers use Singapore for centralized invoicing, our products are almost always shipped elsewhere. Note that over 99% of H100, H200, and Blackwell data center compute revenue billed to Singapore was for orders from U.S.-based customers. Moving to gaming and AI PCs. Gaming revenue was a record $3.8 billion, increasing 48% sequentially and 42% year-on-year. Strong adoption by gamers, creatives, and AI enthusiasts have made Blackwell our fastest ramp ever.

Thanks, Colette. We've had a busy and productive year. Let me share my perspective on some topics we're frequently asked. On export control, China is one of the world's largest AI markets and a springboard to global success. With half of the world's AI researchers based there, the platform that wins China is positioned to lead globally.

Today, however, the $50 billion China market is effectively closed to U.S. industry. The H20 export ban ended our Hopper data center business in China. We cannot reduce Hopper further to comply. As a result, we are taking a multi-billion dollar write-off on inventory that cannot be sold or repurposed. We are exploring limited ways to compete, but Hopper is no longer an option. China's AI moves on with or without U.S. chips. It has to compute to train and deploy advanced models. The question is not whether China will have AI. It already does. The question is whether one of the world's largest AI markets will run on American platforms. Shielding Chinese chip makers from U.S. competition only strengthens them abroad and weakens America's position. Export restrictions have spurred China's innovation and scale. The AI race is not just about chips. It's about which stack the world runs on.

As that stack grows to include 6G and quantum, U.S. global infrastructure leadership is at stake. The U.S. has based its policy on the assumption that China cannot make AI chips. That assumption was always questionable, and now it's clearly wrong. China has enormous manufacturing capability. In the end, the platform that wins the AI developers wins AI. Export controls should strengthen U.S. platforms, not drive half of the world's AI talent to rivals. On DeepSeek, DeepSeek and QN from China are among the best open-source AI models. Released freely, they've gained traction across the U.S., Europe, and beyond. DeepSeek R1, like ChatGPT, introduced Reasoning AI that produces better answers the longer it thinks. Reasoning AI enables step-by-step problem-solving, planning, and tool use, turning models into intelligent agents. Reasoning is compute-intensive, requires hundreds to thousands of times more tokens per task than previous one-shot inference.

Reasoning models are driving a step-function surge in inference demand. AI scaling laws remain firmly intact, not only for training, but now inference too requires massive-scale compute. DeepSeek also underscores the strategic value of open-source AI. When popular models are trained and optimized on U.S. platforms, it drives usage, feedback, and continuous improvement, reinforcing American leadership across the stack. U.S. platforms must remain the preferred platform for open-source AI. That means supporting collaboration with top developers globally, including in China. America wins when models like DeepSeek and QN run best on American infrastructure. Regarding onshore manufacturing, President Trump has outlined a bold vision to reshore advanced manufacturing, create jobs, and strengthen national security. Future plants will be highly computerized and robotics. We share this vision. TSMC is building six fabs and two advanced packaging plants in Arizona to make chips for NVIDIA.

Process qualification is underway, with volume production expected by year-end. Spill and Amcor are also investing in Arizona, constructing packaging, assembly, and test facilities. In Houston, we're partnering with Foxconn to construct a million-square-foot factory to build AI supercomputers. Wistron is building a similar plant in Fort Worth, Texas. To encourage and support these investments, we've made substantial long-term purchase commitments, a deep investment in America's AI manufacturing future. Our goal: from chip to supercomputer, built in America within a year. Each GB200 NVLink 72 racks contains 1.2 million components and weighs nearly 2 tons. No one has produced supercomputers on this scale. Our partners are doing an extraordinary job. On AI diffusion rule, President Trump rescinded the AI diffusion rule, calling it counterproductive, and proposed a new policy to promote U.S. AI tech with trusted partners. On his Middle East tour, he announced historic investments.

I was honored to join him in announcing a 500 MW AI infrastructure project in Saudi Arabia and a 5 GW AI campus in the UAE. President Trump wants U.S. tech to lead. The deals he announced are wins for America: creating jobs, advancing infrastructure, generating tax revenue, and reducing the U.S. trade deficit. The U.S. will always be NVIDIA's largest market and home to the largest installed base of our infrastructure. Every nation now sees AI as core to the next industrial revolution, a new industry that produces intelligence and essential infrastructure for every economy. Countries are racing to build national AI platforms to elevate their digital capabilities. At Computex, we announced Taiwan's first AI factory in partnership with Foxconn and the Taiwan government. Last week, I was in Sweden to launch its first national AI infrastructure.

Japan, Korea, India, Canada, France, the U.K., Germany, Italy, Spain, and more are now building national AI factories to empower startups, industries, and societies. Sovereign AI is a new growth engine for NVIDIA. Toshiya, back to you. Thank you.

Operator, we will now open the call for questions. Would you please pull for questions?