The company reported a record $68.1 billion quarter, shipped its first Vera Rubin samples to customers, and gave guidance suggesting the AI infrastructure boom is still accelerating.
By LDS Team
February 25, 2026
On February 25, 2026, NVIDIA did two things that would have been unthinkable five years ago. First, it reported $68.1 billion in revenue for a single quarter -- the largest in the history of the semiconductor industry. Then, almost as a footnote on the earnings call, CFO Colette Kress mentioned something that mattered even more: "We shipped our first Vera Rubin samples to customers earlier this week."
Vera Rubin is NVIDIA's next-generation AI chip platform. It is the successor to Blackwell, which has been the engine behind the current AI infrastructure boom. If Blackwell was the chip that proved AI could be a hundred-billion-dollar business, Vera Rubin is the one NVIDIA is betting will make it a trillion-dollar one.
The timing is deliberate. NVIDIA's GTC 2026 conference kicks off March 16 in San Jose. CEO Jensen Huang has promised to unveil something that will "surprise the world." The samples shipping this week are a preview -- a signal to the market, to competitors, and to every company building AI infrastructure: the next generation is here.
What Vera Rubin Actually Is
Vera Rubin is not just a GPU. It is a six-chip platform -- the most complex NVIDIA has ever built.
The platform is named after Vera Florence Cooper Rubin (1928-2016), the American astronomer who provided the first strong observational evidence for dark matter by studying galaxy rotation curves in the 1970s. NVIDIA has a tradition of naming GPU architectures after scientists -- from Tesla and Fermi to Ada Lovelace and Grace Hopper. Rubin continues the recent trend of honoring women who transformed their fields.
The six components:
- Rubin GPU -- The compute engine. Built on TSMC's 3nm process, with 336 billion transistors packed across two reticle-sized compute chiplets and two I/O dies.
- Vera CPU -- An 88-core custom Arm processor designed specifically to pair with the Rubin GPU. It is the first CPU to natively support FP8 precision.
- NVLink 6 -- The interconnect fabric, delivering 3.6 TB/s per GPU -- enough bandwidth to make dozens of GPUs behave like a single massive processor.
- ConnectX-9 SuperNIC, BlueField-4 DPU, NVLink 6 Switch, and Spectrum-6 Ethernet Switch -- The networking components that tie the system together at rack scale.
When assembled into NVIDIA's NVL72 configuration -- 72 Rubin GPUs and 36 Vera CPUs in a single rack -- the system delivers 3.6 exaflops of FP4 compute and 260 TB/s of internal bandwidth. NVIDIA says that bandwidth figure exceeds the entire internet's current capacity.
CNBC, which received an exclusive first look at the physical hardware, reported that each Vera Rubin system contains 1.3 million components from more than 80 suppliers across 20 countries.
Worth noting: "Vera Rubin" refers to the combined CPU-GPU superchip. "Vera" is the CPU. "Rubin" is the GPU. They connect via NVLink-C2C at 1.8 TB/s -- double the bandwidth of the previous Grace Blackwell pairing.
The Specs
Here is what NVIDIA has revealed, compared to the current-generation Blackwell B200:
| Spec | Vera Rubin | Blackwell (B200) | Improvement |
|---|---|---|---|
| Inference (FP4) | 50 PFLOPS | ~10 PFLOPS | 5x |
| Training (FP4) | 35 PFLOPS | ~10 PFLOPS | 3.5x |
| Memory | 288 GB HBM4 | 192 GB HBM3e | 1.5x |
| Memory Bandwidth | 22 TB/s | 8 TB/s | 2.8x |
| Transistors | 336 billion | 208 billion | 1.6x |
| NVLink Bandwidth | 3.6 TB/s per GPU | 1.8 TB/s per GPU | 2x |
| Process Node | TSMC 3nm (N3P) | TSMC 4nm | -- |
| TDP (reported) | ~2,300W | 1,200W | -- |
The Vera CPU brings 88 custom "Olympus" Arm cores with 176 threads via Spatial Multithreading, up to 1.5 TB of LPDDR5X memory, and 1.2 TB/s of memory bandwidth. Its performance is roughly double the Grace CPU it replaces.
In the full NVL72 rack -- 72 GPUs, 36 CPUs, one enclosure -- the system delivers 3.6 exaflops of FP4 compute, 20.7 TB of total HBM4 memory, and 260 TB/s of scale-up bandwidth. NVIDIA claims up to 10x lower cost per token and 4x fewer GPUs needed for mixture-of-experts training compared to Blackwell.
Worth noting: The reported ~2,300-watt TDP per GPU (per analyst estimates; NVIDIA has not officially confirmed this figure) is nearly double Blackwell's. Data centers will need significant infrastructure upgrades to run Vera Rubin at scale. NVIDIA claims the system-level efficiency improvements offset the raw power increase, but the absolute power draw is a real constraint for deployment.
The Biggest Quarter in Semiconductor History
The Vera Rubin sample shipment came on the same day NVIDIA reported financial results that broke its own records.
| Metric | Q4 FY2026 | Year-Over-Year |
|---|---|---|
| Revenue | $68.1 billion | +73% |
| Data Center Revenue | $62.3 billion | +75% |
| Net Income | $43.0 billion | ~+94% |
| EPS (adjusted) | $1.62 | +82% |
| Q1 FY2027 Guidance | $78.0 billion | Beat estimates by $5.4B |
For the full fiscal year 2026 (ended January 2026), NVIDIA reported $215.9 billion in total revenue, up 65% year-over-year. Data center alone accounted for $193.7 billion -- roughly 90% of the total. Hyperscalers represent just over half of data center revenue.
The quarterly acceleration is striking. Q1: $44.1 billion. Q2: $46.7 billion. Q3: $57.0 billion. Q4: $68.1 billion. Each quarter larger than the last, with no sign of deceleration. The Q1 FY2027 guidance of $78 billion -- beating analyst estimates by $5.4 billion -- suggests the trend is continuing.
NVIDIA's market capitalization sits at approximately $4.7 trillion, making it the most valuable company in the world. Its order backlog exceeds $500 billion and continues to grow as customers place full-year orders for Vera Rubin.
Huang framed the economics bluntly on the earnings call: "Compute is revenues. Without compute, there is no way to generate tokens. Without tokens, there's no way to grow revenues."
Kress added: "We expect every cloud model builder to deploy Vera Rubin."
The Road to Vera Rubin
Everyone Wants One
The list of confirmed Vera Rubin deployment partners reads like a directory of the world's most valuable technology companies.
Cloud providers (first wave, H2 2026): AWS, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure, CoreWeave, Lambda, Nebius, and Nscale.
AI labs: Meta has committed to deploying "millions of Blackwell and Rubin GPUs." Anthropic will train and run inference on Vera Rubin systems. OpenAI, xAI, Mistral AI, Cohere, and Perplexity are all expected adopters.
Infrastructure partners: Dell, HPE, Lenovo, Cisco, and Supermicro will build server systems around the platform.
Microsoft is planning deployments of "hundreds of thousands of Vera Rubin Superchips" across its Fairwater AI superfactory sites.
The spending commitments behind these deployments are staggering. The four largest hyperscalers have collectively guided for $635-665 billion in capital expenditure for 2026:
| Company | 2026 CapEx (Guided) |
|---|---|
| Amazon | ~$200 billion |
| Alphabet/Google | $175-185 billion |
| Microsoft | ~$145 billion (analyst estimate) |
| Meta | $115-135 billion |
That is a 67-74% increase over 2025 levels. Roughly three-quarters of it -- around $450 billion -- is directly tied to AI infrastructure: servers, GPUs, and data centers.
Worth noting: Amazon's AI infrastructure spending is so aggressive that analysts project the company will run negative free cash flow of $17-28 billion in 2026. The hyperscalers are increasingly turning to debt markets to fund AI capex, transforming what were historically cash-rich businesses into leveraged ones. The question of whether this spending generates proportional returns is one nobody can answer yet.
The Competition Is Real
NVIDIA holds an estimated 86-95% of the AI training chip market depending on the measure. But for the first time, credible alternatives are emerging on multiple fronts.
AMD is closest. At CES 2026, AMD unveiled Helios -- its direct rack-scale competitor to the NVL72. The MI400 series chips inside it feature 432 GB of HBM4 memory (50% more than Rubin's 288 GB), 19.6 TB/s bandwidth, and 40 PFLOPS of FP4 compute. AMD is targeting Helios shipments for H2 2026 -- potentially before Vera Rubin reaches volume production. Oracle has committed to 50,000 MI450 series chips, and OpenAI has partnered with AMD on a massive 6-gigawatt, $90 billion-plus data center and computing infrastructure deal.
Custom silicon is the bigger threat. Every major hyperscaler is now building its own AI chips:
| Company | Custom Chip | Status |
|---|---|---|
| TPU Trillium (v6) | Generally available. Anthropic signed the largest TPU deal in Google's history. | |
| Amazon | Trainium2 / Trainium3 | Trainium2 deployed (~500K chips). Trainium3 ramping early 2026. |
| Microsoft | Maia 200 | Announced January 2026 on TSMC 3nm. Claims 3x Trainium3 inference performance. |
| Meta | MTIA v2 / v3 | v3 due H2 2026. Targets 35%+ of Meta's inference fleet on custom silicon by year-end. |
Custom ASIC shipments are projected to grow 44.6% in 2026, versus 16.1% growth for GPUs. Analysts project custom AI server ASICs could surpass GPU shipments by 2028.
Intel has effectively exited. The company cancelled its Falcon Shores data center GPU in January 2025 after failing to gain meaningful traction with Gaudi 3. Its replacement, Jaguar Shores, is not expected until late 2026 at the earliest. Intel is not a factor in the AI accelerator race.
But one number explains why NVIDIA is not panicking: CUDA has over 4 million developers and thousands of optimized applications. The switching cost is enormous. And in February 2026, Meta -- despite years of investment in its own MTIA chips -- signed a deal to buy millions more GPUs from NVIDIA anyway.
Worth noting: The custom silicon trend cuts both ways. Google's TPUs power Gemini. Amazon's Trainium runs Anthropic's Claude. But both Google and Amazon remain massive NVIDIA customers. Custom chips are supplementing NVIDIA, not replacing it -- at least not yet. The real question is whether that changes when custom ASICs reach performance parity, which some analysts project could happen by 2028.
The Bigger Picture
The AI infrastructure buildout is now the largest technology investment in history.
The combined capital expenditure of the four largest hyperscalers in 2026 -- $635-665 billion -- exceeds the GDP of most countries. Jensen Huang has framed it as the beginning, not the peak. At CES, he described AI infrastructure as an $85 trillion opportunity over the next 15 years and denied that the current spending represents a bubble.
There is evidence on both sides.
The demand for AI compute is genuinely explosive. NVIDIA has a $500 billion-plus backlog that keeps growing. Inference costs are falling fast enough to unlock entirely new applications. Agentic AI -- systems that take autonomous actions, not just generate text -- is creating what Huang calls "easily 100 times more" compute demand than the industry expected a year ago. At the GTC 2025 keynote, he argued that the shift from single-shot answers to multi-step reasoning has fundamentally changed the math on how much compute the world needs.
But hyperscalers are taking on unprecedented debt to fund infrastructure that may not generate proportional revenue for years. Custom silicon threatens NVIDIA's pricing power. And the history of technology is littered with infrastructure booms that ended in correction -- from fiber optics in 2000 to crypto mining rigs in 2018.
NVIDIA's answer to this is speed. Its one-year cadence -- Blackwell (2024), Vera Rubin (2026), Rubin Ultra (2027), Feynman (2028) -- is designed to make the competition irrelevant before it arrives. Rubin Ultra will scale to NVL576 racks with 576 GPUs, delivering 15 exaflops of FP4 compute with up to 1 TB of HBM4e memory per GPU. By the time competitors match Vera Rubin, NVIDIA plans to be two generations ahead.
As one industry analyst put it: "If NVIDIA maintains this cadence, it will be even more difficult for competitors to catch up."
The Bottom Line
NVIDIA just reported the largest quarter in semiconductor history and shipped the first samples of the most powerful AI chip ever made. Its market cap stands at $4.7 trillion. Its backlog exceeds half a trillion dollars. And its guidance says growth is accelerating, not slowing.
Vera Rubin is a genuine generational leap: 5x the inference performance of Blackwell, 2.8x the memory bandwidth, the first GPU to use HBM4, and an entirely new 88-core Arm CPU designed from scratch to pair with it. Every major cloud provider, every major AI lab, and every major infrastructure partner has signed up to deploy it. The question is not whether Vera Rubin will sell. It is whether NVIDIA can make enough of them.
The competition is more credible than it has ever been. AMD's Helios could ship before Vera Rubin reaches volume. Custom ASICs from Google, Amazon, Microsoft, and Meta are growing nearly three times faster than GPUs. And the $635 billion in hyperscaler capex for 2026 suggests the market may be big enough for multiple winners.
But NVIDIA's annual release cadence, the CUDA ecosystem's 4 million developers, and a $500 billion backlog create a moat that nobody has come close to breaching. GTC 2026 is three weeks away. Jensen Huang has promised to "surprise the world."
Given what he just shipped, that is a remarkable thing to say.
Sources
- NVIDIA: Financial Results for Fourth Quarter and Fiscal 2026 (Official) (Feb 25, 2026)
- CNBC: First Look at NVIDIA's AI System Vera Rubin and How It Beats Blackwell (Feb 25, 2026)
- CNBC: NVIDIA Q4 2026 Earnings Report (Feb 25, 2026)
- Tom's Hardware: NVIDIA Delivers First Vera Rubin AI GPU Samples to Customers (Feb 25, 2026)
- Tom's Hardware: NVIDIA Launches Vera Rubin NVL72 at CES (Jan 2026)
- Tom's Hardware: NVIDIA's Vera Rubin Platform In-Depth (2026)
- VideoCardz: NVIDIA Vera Rubin NVL72 Detailed -- 72 GPUs, 36 CPUs, 260 TB/s (2026)
- Fortune: NVIDIA Q4 Earnings Results (Feb 25, 2026)
- NVIDIA Newsroom: Rubin Platform AI Supercomputer (2026)
- CNBC: Tech AI Spending Approaches $700B in 2026 (Feb 6, 2026)
- Tom's Hardware: NVIDIA Announces Rubin GPUs in 2026, Rubin Ultra in 2027, Feynman After (Mar 2025)
- Microsoft Azure Blog: Large-Scale NVIDIA Rubin Deployments (2026)