Huang Renxun's full GTC speech: The era of inference has arrived, with revenue expected to reach at least one trillion dollars by 2027, and lobster is the new operating system

On March 16, 2026, the NVIDIA GTC 2026 conference officially opened, with NVIDIA founder and CEO Jensen Huang delivering the keynote speech.

At this conference, regarded as the "annual pilgrimage of the AI industry," Huang elaborated on NVIDIA's transformation from a "chip company" to an "AI infrastructure and factory company." Addressing the market's concerns about performance sustainability and growth potential, Huang detailed the underlying business logic driving future growth—"Token Factory Economics."

Performance guidance is extremely optimistic, "At least $1 trillion in demand by 2027"

In the past two years, global AI computing demand has exploded exponentially. As large models evolve from "perception" and "generation" to "reasoning" and "action (task execution)," the consumption of computing power has surged dramatically. In response to market concerns about order and revenue ceilings, Huang provided very strong expectations.

Huang stated in his speech:

Last year at this time, I mentioned that we saw a high-confidence demand of $500 billion, covering Blackwell and Rubin until 2026. Now, right here and now, I see at least $1 trillion in demand by 2027.

Huang's trillion-dollar expectation once pushed NVIDIA's stock price up over 4.3%.

Moreover, he further supplemented this figure:

Is this reasonable? That's what I'm going to talk about next. In fact, we may even face supply shortages. I'm sure the actual computing demand will be much higher.

Huang pointed out that today's NVIDIA systems have proven themselves to be the world's "lowest-cost infrastructure." Because NVIDIA can run AI models across almost all fields, this versatility ensures that the $1 trillion invested by customers can be fully utilized and maintained over a long lifecycle.

Currently, 60% of NVIDIA's business comes from the top five hyperscale cloud service providers, while the remaining 40% is widely distributed across sovereign clouds, enterprises, industries, robotics, and edge computing.

Token Factory Economics, where performance per watt determines the lifeblood of business

To explain the reasonableness of this $1 trillion demand, Huang presented a new business mindset to CEOs of global companies. He pointed out that future data centers will no longer be warehouses for storing files, but "factories" producing Tokens (the basic unit generated by AI).

Huang emphasized:

Every data center, every factory, is defined as being limited by power. A 1GW (gigawatt) factory will never become a 2GW factory; this is a law of physics and atoms. At fixed power, whoever has the highest token throughput per watt will have the lowest production costs.

Huang categorized future AI services into four business tiers:

• Free tier (high throughput, low speed)
• Mid-tier (~$3 per million tokens)
• High-tier (~$6 per million tokens)
• High-speed tier (~$45 per million tokens)
• Ultra-high-speed tier (~$150 per million tokens)

He noted that as models grow larger and contexts become longer, AI will become smarter, but the token generation rate will decrease. Huang stated:

In this Token Factory, your throughput and token generation speed will directly translate into your precise revenue for next year.

Huang emphasized that NVIDIA's architecture allows customers to achieve extremely high throughput in the free tier while achieving an astonishing 35 times performance improvement at the highest value inference tier.

Vera Rubin achieves 350 times acceleration in two years, Groq fills the gap for ultra-fast inference

Under the constraints of physical limits, NVIDIA introduced its most complex AI computing system ever, Vera Rubin. Huang stated:

In the past, when mentioning Hopper, I would hold up a chip, which was nice. But when mentioning Vera Rubin, everyone thinks of the entire system. In this 100% liquid-cooled system, which completely eliminates traditional cabling, racks that used to take two days to install now only take two hours.

Huang pointed out that through extreme end-to-end hardware-software co-design, Vera Rubin created an astonishing data leap within the same 1GW data center:

In just two years, we have increased the token generation rate from 22 million to 700 million, achieving a 350-fold growth. Moore's Law during the same period could only bring about a 1.5-fold increase.

To address the bandwidth bottleneck under ultra-fast inference conditions (such as 1000 tokens/second), NVIDIA provided the final solution by integrating the acquired company Groq: asymmetric separated inference. Huang explained:

These two processors have completely different characteristics. The Groq chip has 500MB of SRAM, while a Rubin chip has 288GB of memory.

Huang noted that NVIDIA, through the Dynamo software system, assigns the "pre-fill" stage, which requires massive computation and video memory, to Vera Rubin, while the "decoding" stage, which is extremely sensitive to latency, is assigned to Groq. Huang also provided suggestions for enterprise computing power configuration:

If your workload is mainly high throughput, use 100% Vera Rubin; if you have a large number of high-value programming-level token generation needs, allocate 25% of your data center capacity to Groq.

It was revealed that the Groq LP30 chip, manufactured by Samsung, has entered mass production and is expected to ship in the third quarter, while the first Vera Rubin rack is already operational on Microsoft Azure.

In addition, regarding optical interconnect technology, Huang showcased the world's first mass-produced Co-Packaged Optical (CPO) switch, Spectrum X, and quelled market concerns about the "copper-to-optical transition" route:

We need more copper cable capacity, more optical chip capacity, and more CPO capacity.

Agent ends traditional SaaS, "salary + Token" becomes standard in Silicon Valley

In addition to hardware barriers, Huang devoted a significant portion of his speech to the revolution in AI software and ecosystems, particularly the explosion of Agents.

He described the open-source project OpenClaw as "the most popular open-source project in human history," claiming it surpassed the achievements of Linux over the past 30 years in just a few weeks. Huang bluntly stated that OpenClaw is essentially the "operating system" for agent computers.

Huang asserted:

Every SaaS (Software as a Service) company will transform into an AaaS (Agent-as-a-Service) company. There is no doubt that to safely deploy these agents, which have the ability to access sensitive data and execute code, NVIDIA has launched an enterprise-level NeMo Claw reference design, which adds a policy engine and privacy router.

For ordinary professionals, this transformation is also just around the corner. Huang envisioned a new workplace form in the future:

In the future, every engineer in our company will need an annual token budget. Their base salary may be hundreds of thousands of dollars, and I will allocate about half of that amount as a token quota to them, allowing them to achieve a 10x efficiency increase. This has already become a new hiring chip in Silicon Valley: how many tokens come with your offer?

At the end of the speech, Huang also "spoiled" the next-generation computing architecture, Feynman, which will achieve the first-ever joint horizontal scaling of copper wires and CPOs. More intriguingly, NVIDIA is developing a data center computer for space, "Vera Rubin Space-1," which completely opens up the imagination of AI computing power extending beyond Earth.

The full text of Jensen Huang's GTC 2026 speech is as follows (with AI tools assistance):

Host: Welcome NVIDIA founder and CEO Jensen Huang to the stage.

Jensen Huang, Founder and CEO:

Welcome to GTC. I want to remind everyone that this is a technology conference. I am very pleased to see so many people lining up to enter early in the morning and to see all of you here.

At GTC, we will focus on three major themes: technology, platform, and ecosystem. NVIDIA currently has three major platforms: the CUDA-X platform, the systems platform, and our newly launched AI factory platform.

Before we officially begin, I want to thank our warm-up session hosts—Sarah Guo from Conviction, Alfred Lin from Sequoia Capital (NVIDIA's first venture capitalist), and Gavin Baker, NVIDIA's first major institutional investor. These three individuals have profound insights into technology and a wide influence across the entire technology ecosystem. Of course, I also want to thank all the distinguished guests I personally invited to attend today. Thank you to this all-star team.

I also want to thank all the companies present today. NVIDIA is a platform company, and we have technology, platforms, and a rich ecosystem. The companies present today represent almost all participants in the $100 trillion industry, with 450 companies sponsoring this event, for which I am deeply grateful.

This conference features 1,000 technical forums and 2,000 speakers, covering every level of the AI "five-layer cake" architecture—from infrastructure such as land, power, and data centers, to chips, platforms, models, and various applications that ultimately drive the entire industry forward.

CUDA: Two decades of technological accumulation

Everything starts here. This year marks the 20th anniversary of CUDA.

For the past twenty years, we have been dedicated to the development of this architecture. CUDA is a revolutionary invention—SIMT (Single Instruction Multiple Threads) technology allows developers to write programs in scalar code and extend them into multi-threaded applications, with programming difficulty far lower than that of previous SIMD architectures. We have recently added the Tiles feature to help developers program tensor cores more conveniently, as well as various mathematical operation structures relied upon by today's AI. Currently, CUDA has thousands of tools, compilers, frameworks, and libraries, with hundreds of thousands of public projects in the open-source community, and it has been deeply integrated into every technology ecosystem.

This chart reveals NVIDIA's 100% strategic logic, which I have been discussing since the beginning. The most difficult and core element is the "installed base" at the bottom of the chart. Over the past twenty years, we have accumulated hundreds of millions of GPUs and computing systems running CUDA worldwide.

Our GPUs cover all cloud platforms and serve almost all computer manufacturers and industries. The large installed base of CUDA is the fundamental reason why this flywheel continues to accelerate. The installed base attracts developers, developers create new algorithms and breakthroughs, breakthroughs spawn new markets, new markets form new ecosystems and attract more companies to join, thereby expanding the installed base—this flywheel is continuously accelerating.

The download volume of NVIDIA libraries is growing at an astonishing rate, large in scale and increasing in speed. This flywheel enables our computing platform to support massive applications and continuous new breakthroughs.

More importantly, it also gives these infrastructures an extremely long lifespan. The reason is obvious: there are a wealth of applications that can run on NVIDIA CUDA, covering every stage of the AI lifecycle, various data processing platforms, and various scientific principle solvers. Therefore, once NVIDIA GPUs are installed, their actual use value is extremely high. This is also why the cloud price of the Ampere architecture GPU we released six years ago has actually been rising.

The fundamental reason for all this is: a large installed base, a strong flywheel, and a broad developer ecosystem. When these factors work together, coupled with our continuous software updates, computing costs will continue to decline. Accelerated computing significantly enhances application performance, and as we maintain and iterate software over the long term, users not only gain performance leaps initially but also continue to enjoy declining computing costs. We are willing to provide long-term support for every GPU globally because they are fully compatible at the architectural level.

We are willing to do this because the installed base is so large—every time a new optimization is released, it benefits millions of users. This dynamic combination allows NVIDIA's architecture to continuously expand its coverage, accelerate its own growth, and continuously lower computing costs, ultimately stimulating new growth. CUDA is at the core of all this.

From GeForce to CUDA: A 25-year evolution

Our journey with CUDA actually began 25 years ago.

GeForce—many of you have grown up with GeForce. GeForce is NVIDIA's most successful marketing project. We started cultivating future customers when you couldn't afford our products—your parents became NVIDIA's earliest users, purchasing our products year after year, until one day you grew up to become excellent computer scientists, becoming true customers and developers.

This is the foundation laid by GeForce 25 years ago. Twenty-five years ago, we invented programmable shaders—an obvious yet profoundly significant invention that made accelerators programmable, and the world's first programmable accelerator, namely pixel shaders. Five years later, we created CUDA—one of our most important investments ever. At that time, the company had limited financial resources, but we bet most of our profits on this, committed to extending CUDA from GeForce to every computer. We were so determined because we believed in its potential. Despite facing hardships in the early stages, the company held this belief for 13 generations, a full twenty years, and today CUDA is everywhere.

It was the pixel shader that drove the revolution of GeForce. About eight years ago, we launched RTX—a comprehensive overhaul of architecture for the modern computer graphics era. GeForce brought CUDA to the world, and because of this, many scholars such as Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton, and Andrew Ng discovered that GPUs could become powerful tools for accelerating deep learning, igniting the AI explosion a decade ago.

A decade ago, we decided to merge programmable shading with two new concepts: one was hardware ray tracing, which is technically challenging; the other was a forward-looking idea—about ten years ago, we foresaw that AI would fundamentally transform computer graphics. Just as GeForce brought AI to the world, AI is now reshaping the way computer graphics are implemented.

Today, I want to show you the future. This is our next-generation graphics technology, which we call neural rendering—deep integration of 3D graphics and artificial intelligence. This is DLSS 5, please take a look.

Neural Rendering: The fusion of structured data and generative AI

Isn't this breathtaking? Computer graphics are thus revitalized.

What did we do? We combined controllable 3D graphics (the real foundation of the virtual world) with its structured data, then infused it with generative AI and probabilistic computing. One is completely deterministic, while the other is probabilistic yet highly realistic—we merged these two concepts into one, achieving precision control through structured data while generating in real-time. Ultimately, the content is both visually stunning and fully controllable.

The idea of merging structured information with generative AI will continue to manifest across various industries. Structured data is the cornerstone of trustworthy AI.

Accelerated platform for structured and unstructured data

Now I want to show you a technical architecture diagram.

Structured data—familiar SQL, Spark, Pandas, Velox, and important platforms like Snowflake, Databricks, Amazon EMR, Azure Fabric, Google BigQuery, all handle data frames. These data frames are like giant spreadsheets, carrying all the information of the business world, serving as the basic facts (Ground Truth) of enterprise computing.

In the AI era, we need to let AI use structured data and achieve extreme acceleration. In the past, accelerating structured data processing was aimed at making enterprises operate more efficiently. In the future, AI will use these data structures at speeds far exceeding human capabilities, and AI agents will heavily rely on structured databases.

Regarding unstructured data, vector databases, PDFs, videos, audio, etc., constitute the vast majority of data forms in the world—about 90% of the data generated each year is unstructured. In the past, this data was almost entirely unusable: we read it, stored it in file systems, and that was it. We couldn't query it, nor could we retrieve it, because unstructured data lacks simple indexing methods and must be understood in terms of meaning and context. Now, AI can do this—thanks to multimodal perception and understanding technologies, AI can read PDF documents, understand their meanings, and embed them into larger structures for querying.

NVIDIA has created two foundational libraries for this purpose:

• cuDF: for accelerated processing of data frames and structured data

• cuVS: for vector storage, semantic data, and unstructured AI data processing

These two platforms will become one of the most important foundational platforms in the future.

Today, we announce partnerships with several companies. IBM—the inventor of SQL—will use cuDF to accelerate its WatsonX Data platform. Dell has collaborated with us to create the Dell AI Data Platform, integrating cuDF and cuVS, achieving significant performance improvements in actual projects with NTT Data. On the Google Cloud front, we are now not only accelerating Vertex AI but also BigQuery, and we have partnered with Snapchat to reduce its computing costs by nearly 80%.

The benefits of accelerated computing are threefold: speed, scale, and cost. This aligns with the logic of Moore's Law—achieving performance leaps through accelerated computing while continuously optimizing algorithms, allowing everyone to enjoy continuously declining computing costs.

NVIDIA has built an accelerated computing platform that brings together numerous libraries: RTX, cuDF, cuVS, and more. These libraries are integrated into global cloud services and OEM systems, reaching users worldwide.

Deep collaboration with cloud service providers

Collaboration with major cloud service providers

Google Cloud: We accelerate Vertex AI and BigQuery, deeply integrating with JAX/XLA, while performing excellently on PyTorch—NVIDIA is the only accelerator in the world that performs well on both PyTorch and JAX/XLA. We have brought customers like Base10, CrowdStrike, Puma, and Salesforce into the Google Cloud ecosystem.

AWS: We accelerate EMR, SageMaker, and Bedrock, with deep integration with AWS. This year, I am particularly excited that we will bring OpenAI into AWS, which will significantly boost AWS cloud consumption growth and help OpenAI expand regional deployments and computing scale.

Microsoft Azure: NVIDIA's 100 PFLOPS supercomputer is our first supercomputer built and the first supercomputer deployed on Azure, laying an important foundation for collaboration with OpenAI. We accelerate Azure cloud services and AI Foundry, collaborating to promote Azure regional expansion and deeply cooperating on Bing search. Notably, our confidential computing capability—ensuring that even operators cannot view user data and models—makes NVIDIA GPUs among the first in the world to support confidential computing, enabling confidential deployments of OpenAI and Anthropic models in cloud environments across the globe. For example, we accelerate all EDA and CAD workflows for Synopsys and deploy them on Microsoft Azure.

Oracle: We are Oracle's first AI customer, and I am proud to have been the first to explain the concept of AI cloud to Oracle. Since then, they have developed rapidly, and we have introduced many partners such as Cohere, Fireworks, and OpenAI.

CoreWeave: The world's first AI-native cloud, born for GPU hosting and AI cloud services, with an excellent customer base and strong growth momentum.

Palantir + Dell: The three parties jointly created a new AI platform based on Palantir's ontology platform and AI platform, capable of fully localized deployment of AI in any country and any air-gapped environment—from data processing (vectorization or structuring) to a complete accelerated computing stack for AI.

NVIDIA has established this special cooperative relationship with global cloud service providers—we bring customers to the cloud, creating a mutually beneficial ecosystem.

Vertical integration, horizontal openness: NVIDIA's core strategy

NVIDIA is the world's first vertically integrated and horizontally open company.

The necessity of this model is very simple: accelerated computing is not just a chip issue or a system issue; its complete expression should be application acceleration. CPUs can make computers run faster overall, but this path has reached a bottleneck. In the future, only through application or domain-specific acceleration can we continue to achieve performance leaps and cost reductions.

This is precisely why NVIDIA must deeply cultivate one library after another, one field after another, one vertical industry after another. We are a vertically integrated computing company, and there is no other path to take. We must understand applications, understand domains, deeply understand algorithms, and be able to deploy them in any scenario—data centers, cloud, on-premises, edge, and even robotic systems.

At the same time, NVIDIA remains horizontally open, willing to integrate technology into any partner's platform, allowing the whole world to enjoy the dividends of accelerated computing.

The structure of attendees at this GTC fully reflects this. The proportion of attendees from the financial services industry is the highest—hoping to attract developers, not traders. Our ecosystem covers the entire upstream and downstream supply chain. Whether a company has been established for 50, 70, or 150 years, last year marked its best year in history. We are at the starting point of something very, very significant.

CUDA-X: The accelerated computing engine for various industries

In various vertical fields, NVIDIA has deeply laid out:

• Autonomous driving: Wide coverage and far-reaching impact

• Financial services: Quantitative investment is shifting from manual feature engineering to deep learning driven by supercomputers, ushering in its "Transformer moment"

• Healthcare: It is experiencing its own "ChatGPT moment," covering AI-assisted drug discovery, AI agent-supported diagnostics, medical customer service, and more

• Industry: The largest construction wave globally is unfolding, with AI factories, chip factories, and data center factories being established

• Entertainment and gaming: Real-time AI platforms support translation, live streaming, gaming interaction, and intelligent shopping agents

• Robotics: With over a decade of deep cultivation, three major computing architectures (training computers, simulation computers, onboard computers) are in place, with 110 robots showcased at this exhibition

• Telecommunications: An industry worth about $2 trillion, base stations will evolve from single communication functions to AI infrastructure platforms, with a related platform named Aerial, deeply collaborating with companies like Nokia and T-Mobile

The core of all these fields is our CUDA-X library—this is the fundamental essence of NVIDIA as an algorithm company. These libraries are the company's most core assets, allowing the computing platform to deliver actual value across various industries.

One of the most important libraries is cuDNN (CUDA Deep Neural Network Library), which has completely revolutionized artificial intelligence, triggering the modern AI explosion.

(Play CUDA-X demonstration video)

Everything you just saw was simulation—including physics-based solvers, AI agent physical models, and physical AI robot models. Everything was simulated, with no manual animation or joint binding. This is precisely where NVIDIA's core capability lies: unlocking these opportunities through a deep understanding of algorithms and organic integration with the computing platform.

AI-native enterprises and the new computing era

You just saw industry giants defining today's society, such as Walmart, L'Oréal, JPMorgan Chase, Roche, and Toyota, as well as a large number of companies you may have never heard of—we call these AI-native enterprises. This list is extensive, including OpenAI, Anthropic, and many emerging companies serving different verticals.

In the past two years, this industry has experienced astonishing growth. The scale of venture capital flowing into startups reached $150 billion, a record high in human history. More importantly, the size of individual investments has jumped from millions of dollars to hundreds of millions and even billions. The reason is simple: for the first time in history, every such company requires massive computing resources and a large number of tokens. This industry is creating and generating tokens or adding value to tokens from organizations like Anthropic and OpenAI.

Just as the PC revolution, internet revolution, and mobile cloud revolution each birthed a batch of epoch-making companies, this generation of computing platform transformation will also give rise to a batch of highly influential companies, becoming an important force in the future world.

Three historic breakthroughs driving all this

What exactly has happened in the past two years? Three major events.

First: ChatGPT, ushering in the era of generative AI (late 2022 to 2023)

It can not only perceive and understand but also generate unique content. I demonstrated the fusion of generative AI with computer graphics. Generative AI fundamentally changes the way computing works—computing has shifted from retrieval-based to generation-based, profoundly impacting computer architecture, deployment methods, and overall significance.

Second: Reasoning AI, represented by o1

Reasoning capabilities enable AI to self-reflect, plan, and decompose problems—breaking down problems it cannot directly understand into manageable steps. o1 makes generative AI trustworthy, capable of reasoning based on real information. To achieve this, the amount of input context tokens and output tokens for thinking has significantly increased, leading to a substantial rise in computing demands.

Third: Claude Code, the first agent model

It can read files, write code, compile, test, evaluate, and iterate. Claude Code has completely revolutionized software engineering—100% of NVIDIA's engineers are using one or more of Claude Code, Codex, and Cursor; there is not a single software engineer who does not leverage AI assistance.

This is a new turning point—you no longer ask AI "what is it, where is it, how to do it," but rather let it "create, execute, build," allowing it to actively use tools, read files, decompose problems, and take action. AI has evolved from perception to generation, to reasoning, and now truly capable of completing tasks.

In the past two years, the computing demand for reasoning has increased by about 10,000 times, and usage has grown by about 100 times. I have always believed that the computing demand has increased by a million times over the past two years—this is a shared feeling among everyone, including OpenAI and Anthropic. If we can obtain more computing power, we can generate more tokens, revenues will increase, and AI will become smarter. The reasoning turning point has indeed arrived.

The trillion-dollar era of AI infrastructure

Last year at this time, I stated here that we had high confidence in the demand and purchase orders for Blackwell and Rubin until 2026, amounting to about $500 billion. Today, one year after GTC, I stand here to tell you: looking ahead to 2027, I see a figure of at least $1 trillion. And I am confident that the actual computing demand will be far beyond this.

2025: The Year of Inference for NVIDIA

2025 is NVIDIA's Year of Inference. We want to ensure that, beyond training and post-training, we maintain excellence at every stage of the AI lifecycle, allowing the invested infrastructure to operate efficiently and effectively for longer, with lower unit costs.

At the same time, Anthropic and Meta have officially joined the NVIDIA platform, together representing one-third of global AI computing demand. Open-source models are nearing the cutting edge and are ubiquitous.

NVIDIA is currently the only platform in the world capable of running all AI fields—language, biology, computer graphics, computer vision, speech, protein and chemistry, robotics, etc.—all AI models, whether at the edge or in the cloud, regardless of language. NVIDIA's architecture is universal across all these scenarios, making us the lowest-cost and highest-confidence platform.

Currently, 60% of NVIDIA's business comes from the top five hyperscale cloud service providers, while the remaining 40% is distributed across regional clouds, sovereign clouds, enterprises, industries, robotics, and edge computing. The breadth of AI coverage itself is its resilience—this is undoubtedly a new computing platform transformation.

Grace Blackwell and NVLink 72: Bold architectural innovation

While the Hopper architecture was still at its peak, we decided to completely re-architect the system, expanding NVLink from 8 lanes to NVLink 72, fully decomposing and reconstructing the computing system. Grace Blackwell NVLink 72 is a significant technological bet, not easy for all partners, and I sincerely thank everyone for that.

At the same time, we launched NVFP4—not just an ordinary FP4, but a new type of tensor core and computing unit. We have demonstrated that NVFP4 can achieve inference without any loss of precision while delivering significant performance and energy efficiency improvements, and it is also suitable for training. Additionally, a series of new algorithms such as Dynamo and TensorRT-LLM have emerged, and we even invested billions of dollars to build a supercomputer specifically for optimizing kernels, called DGX Cloud.

The results show that our inference performance is remarkable. Data from Semi Analysis—the most comprehensive AI inference performance evaluation to date—shows that NVIDIA leads significantly in both tokens per watt and cost per token. Originally, Moore's Law might have provided a 1.5-fold performance boost for H200, but we achieved 35 times. Semi Analysis's Dylan Patel even said, "Jensen sandbagged; it's actually 50 times." He is right.

I quote him: "Jensen sandbagged."

NVIDIA's cost per token is the lowest in the world, currently unmatched. The reason lies in extreme co-design.

For example, before NVIDIA updated the entire suite of software and algorithms, Fireworks had an average token speed of about 700 per second; after the update, it approached 5,000 per second, an increase of about 7 times. This is the power of extreme co-design.

AI Factory: From data centers to token factories

Data centers used to be places for storing files; now they are factories for producing tokens. Every cloud service provider and every AI company will use "token factory efficiency" as a core operational metric in the future.

This is my core argument:

• Vertical axis: Throughput—number of tokens generated per second at fixed power

• Horizontal axis: Interaction speed—response speed for each inference; the faster the speed, the larger the usable model, the longer the context, and the smarter the AI

Tokens are the new commodity, and once mature, will be priced in tiers:

• Free tier (high throughput, low speed)

• Mid-tier (~$3 per million tokens)

• High-tier (~$6 per million tokens)

• High-speed tier (~$45 per million tokens)

• Ultra-high-speed tier (~$150 per million tokens)

Compared to Hopper, Grace Blackwell has improved throughput by 35 times at the highest value tier and introduced a new tier. Simplifying model estimates, if 25% of power is allocated to each of the four tiers, Grace Blackwell could generate 5 times more revenue than Hopper.

Vera Rubin: The next-generation AI computing system

(Play Vera Rubin system introduction video)

Vera Rubin is a complete, end-to-end optimized system designed for agentic workloads:

• Large language model computing core: NVLink 72 GPU cluster, handling pre-fill and KV Cache

• New Vera CPU: Designed for extremely high single-thread performance, using LPDDR5 memory, with excellent energy efficiency, the world's only data center CPU using LPDDR5, suitable for AI agent tool calls

• Storage system: BlueField 4 + CX 9, a new storage platform for the AI era, with 100% participation from the global storage industry

• CPO Spectrum X switch: The world's first co-packaged optical Ethernet switch, now in full mass production

• Kyber rack: A new rack system supporting 144 GPUs to form a single NVLink domain, with front-end computing and back-end NVLink switching, forming a giant computer

• Rubin Ultra: Next-generation supercomputer node, vertical design, paired with the Kyber rack, supporting larger-scale NVLink interconnections

Vera Rubin is 100% liquid-cooled, reducing installation time from two days to two hours, using 45°C hot water cooling, significantly alleviating cooling pressure in data centers. This time, Satya (Nadella) has confirmed that the first Vera Rubin rack is now operational on Microsoft Azure, which I find very exciting.

Groq integration: Extreme extension of inference performance

We acquired the Groq team and obtained its technology license. Groq is a deterministic data flow processor, utilizing static compilation and compiler scheduling, with a large amount of SRAM, optimized for single workload inference, featuring extremely low latency and high token generation speed.

However, Groq's memory capacity is limited (500MB on-chip SRAM), making it difficult to independently carry the parameters and KV Cache of large models, limiting its large-scale application.

The solution is Dynamo—a set of inference scheduling software. We disaggregated the inference pipeline through Dynamo:

• Pre-fill and attention mechanism decoding are completed on Vera Rubin (requiring massive computing power and KV Cache storage)

• Feed-forward network decoding, i.e., the token generation part, is completed on Groq (requiring extremely high bandwidth and low latency)

The two are tightly coupled via Ethernet, reducing latency by about half through special modes. Under the unified scheduling of Dynamo, the "AI factory operating system," overall performance improves by 35 times, opening up new levels of inference performance previously unreachable by NVLink 72.

Recommendations for the combination of Groq and Vera Rubin:

• If the workload is primarily high throughput, use 100% Vera Rubin

• If a large number of workloads involve high-value token generation such as code generation, introduce Groq, with a recommended ratio of about 25% Groq + 75% Vera Rubin

The Groq LP30, manufactured by Samsung, has entered mass production and is expected to start shipping in Q3. Thanks to Samsung for their full cooperation.

Historic leap in inference performance

Quantifying previous technological advancements: within two years, the token generation rate of a 1GW AI factory will increase from 22 million tokens/second to 700 million tokens/second, a 350-fold increase. This is the power of extreme co-design.

Technology roadmap

• Blackwell: Currently in production, Oberon standard rack system, copper cable expanded to NVLink 72, optional optical expansion to NVLink 576

• Vera Rubin (current): Kyber rack, NVLink 144 (copper cable); Oberon rack, NVLink 72 + optical, expanded to NVLink 576; Spectrum 6, the world's first CPO switch

• Vera Rubin Ultra (coming soon): Next-generation Rubin Ultra GPU, LP35 chip (first integration of NVFP4), further enhancing performance several times

• Feynman (next generation): New GPU, LP40 chip (jointly developed by NVIDIA and the Groq team, integrating NVFP4); new CPU—Rosa (Rosalyn); BlueField 5; CX 10; Kyber rack supporting both copper and CPO expansion methods

The roadmap is clear: copper expansion, optical expansion (Scale-Up), and optical expansion (Scale-Out) are advancing in parallel, and we need all partners to continue expanding production in copper cables, optical fibers, and CPO.

NVIDIA DSX: The digital twin platform for AI factories

AI factories are becoming increasingly complex, but the various technology suppliers that make them up have never collaborated during the design phase, only "meeting" in the data center—this is clearly insufficient.

To address this, we created Omniverse and the NVIDIA DSX platform based on it—a platform for all partners to collaboratively design and operate gigawatt-level AI factories in the virtual world. DSX provides:

• Rack-level mechanical, thermal, electrical, and network simulation systems

• Connection with the power grid for collaborative energy-saving scheduling

• Dynamic power consumption and cooling optimization based on Max-Q within the data center

Conservatively estimated, this system can improve energy utilization efficiency by about 2 times, which is a significant benefit at the scale we are discussing. Omniverse starts from the digital earth and will carry digital twins of various scales; we are building the largest computer in human history in collaboration with global partners.

In addition, NVIDIA is venturing into space. The Thor chip has passed radiation certification and is running in satellites. We are developing Vera Rubin Space-1 with partners for building space data center computers. In space, we can only rely on radiation for heat dissipation, and thermal management is a core challenge; we are gathering top engineers to tackle this.

OpenClaw: The operating system for the agent era

Peter Steinberger developed a software called OpenClaw. This is the most popular open-source project in human history, surpassing Linux's achievements in just a few weeks.

OpenClaw is essentially an agentic system capable of:

• Managing resources, accessing tools, file systems, and large language models

• Executing scheduling and timed tasks

• Gradually decomposing problems and invoking sub-agents

• Supporting arbitrary modalities of input and output (voice, video, text, email, etc.)

Describing it in the syntax of an operating system, it truly is an operating system—the operating system for agent computers. Windows made personal computing possible; OpenClaw makes personal agents possible.

Every enterprise needs to formulate its own OpenClaw strategy, just as we all need Linux strategies, HTML strategies, and Kubernetes strategies.

Comprehensive reshaping of enterprise IT

Before OpenClaw, enterprise IT involved data and files entering systems, flowing through tools and workflows, ultimately becoming tools for human use. Software companies created tools, and system integrators (GSI) and consulting firms helped enterprises use these tools.

After OpenClaw, every SaaS company will transform into an AaaS (Agentic as a Service) company—not just providing tools, but providing AI agents specialized in specific fields.

But there is a key challenge: internal agents can access sensitive data, execute code, and communicate with external parties. This must be strictly controlled in enterprise environments.

To address this, we collaborated with Peter to integrate security into the enterprise version, launching:

• NeMo Claw (reference design): An enterprise-level reference framework based on OpenClaw, integrating NVIDIA's full suite of agent AI toolkits

• Open Shield (security layer): Integrated into OpenClaw, providing policy engines, network barriers, and privacy routers to ensure enterprise data security

• NeMo Cloud: Available for download and integrated with the policy engines of all SaaS companies

This is a renaissance for enterprise IT, a $2 trillion industry poised to grow into a multi-trillion dollar scale, shifting from providing tools to offering specialized AI agent services.

I can fully foresee that in the future, every engineer in a company will have an annual token budget. Their salaries may be hundreds of thousands of dollars, and I will additionally provide them with a token quota equivalent to half their salary, allowing their output to multiply by 10 times. "How many tokens come with your job offer?" has become a new hiring topic in Silicon Valley.

Every enterprise in the future will be both a user of tokens (for engineers) and a producer of tokens (providing services to their customers). The significance of OpenClaw cannot be underestimated; it is as important as HTML and Linux.

NVIDIA Open Model Initiative

In the area of custom agents (Custom Claw), we provide NVIDIA's self-developed cutting-edge models:

Model Domain Nemotron Large Language Model Cosmos World Foundation Model GROOT General Humanoid Robot Model Alpamayo Autonomous Driving BioNeMo Digital Biology Phys-AIAI Physics

We are at the forefront of technology in every field and are committed to continuous iteration—Nemotron 3 will be followed by Nemotron 4, Cosmos 1 will be followed by Cosmos 2, and Groq will also iterate to its second generation.

Nemotron 3 ranks among the top three models globally in OpenClaw and is at the cutting edge. Nemotron 3 Ultra will become the strongest foundational model ever, supporting countries in building sovereign AI.

Today, we announce the establishment of the Nemotron Alliance, investing billions of dollars to advance the development of AI foundational models. Alliance members include: BlackForest Labs, Cursor, LangChain, Mistral, Perplexity, Reflection, Sarvam (India), Thinking Machines (Mira Murati's lab), and more. One after another, enterprise software companies are joining, integrating the NeMo Claw reference design and NVIDIA's agent AI toolkit into their products.

Physical AI and Robotics

Digital agents act in the digital world—writing code, analyzing data; while physical AI refers to embodied agents, i.e., robots.

At this GTC, 110 robots were showcased, almost encompassing all robot development companies worldwide. NVIDIA provides three computers (training computers, simulation computers, onboard computers) and a complete software stack and AI models.

In terms of autonomous driving, the "ChatGPT moment" for autonomous driving has arrived. Today, we announce four new partners joining NVIDIA's RoboTaxi Ready platform: BYD, Hyundai, Nissan, and Geely, with a total annual production of 18 million vehicles. Along with previous partners like Mercedes-Benz, Toyota, and General Motors, the lineup has further expanded. We also announced a significant collaboration with Uber to deploy and integrate RoboTaxi Ready vehicles in multiple cities.

In the field of industrial robots, numerous companies such as ABB, Universal Robotics, and KUKA are collaborating with us to combine physical AI models with simulation systems, promoting the deployment of robots in global manufacturing lines.

In telecommunications, Caterpillar and T-Mobile are also among them. In the future, wireless base stations will no longer just be communication nodes but will become NVIDIA Aerial AI RAN—an intelligent edge computing platform capable of real-time traffic perception and beamforming adjustments to achieve energy-saving and efficiency-enhancing capabilities.

Special segment: Olaf robot appearance

(Play Disney Olaf robot demonstration video)

Jensen Huang: The snowman is here! Newton is running fine! Omniverse is also running fine! Olaf, how are you?

Olaf: I'm really happy to see you.

Jensen Huang: Yes, because I gave you a computer—Jetson!

Olaf: What is that?

Jensen Huang: It's right inside your belly.

Olaf: That's amazing.

Jensen Huang: You learned to walk in Omniverse.

Olaf: I love walking. It's much better than riding a reindeer and looking up at the beautiful sky.

Jensen Huang: That's because of physical simulation—Newton solver running on NVIDIA Warp, developed in collaboration with Disney and DeepMind, allowing you to adapt to the real physical world.

Olaf: I was just about to say that.

Jensen Huang: That's your cleverness. I'm a snowman, not a snowball.

Jensen Huang: Can you imagine? The future Disneyland—all these robotic characters walking freely in the park. But honestly, I thought you would be taller. I've never seen such a short snowman.

Olaf: (noncommittal)

Jensen Huang: Can you help me wrap up today's speech?

Olaf: That would be awesome!

Keynote summary

Jensen Huang: Today, we explored the following core themes together:

1. The arrival of the reasoning turning point: reasoning has become the core workload of AI, tokens are the new commodity, and inference performance directly determines revenue.

2. The era of AI factories: data centers have evolved from file storage facilities to token production factories, and in the future, every company will measure its competitiveness by "AI factory efficiency."

3. The OpenClaw agent revolution: OpenClaw has ushered in the era of agent computing, and enterprise IT is transitioning from the tool era to the agent era; every enterprise needs to formulate an OpenClaw strategy.

4. Physical AI and robotics: Embodied intelligence is being scaled up, with autonomous driving, industrial robots, and humanoid robots collectively forming the next significant opportunity for physical AI.

Thank you all, and enjoy GTC!