NVIDIA Corporation (NVDA)
Bull Thesis
- Parabolic AI demand. Q1 FY27 revenue hit $81.6B (+85% YoY), with data center revenue reaching $75.2B (+92% YoY), as agentic AI workloads drove unprecedented compute demand.1
- Blackwell cycle still early. GB200 and GB300 NVL72 racks are sold out through mid-2026 with a backlog of 3.6 million units from hyperscalers alone, suggesting the upgrade cycle has multi-year runway.2
- Hyperscaler capex surging. The four major hyperscalers (Microsoft, Google, Meta, Amazon) are collectively targeting ~$380B+ in 2026 AI capex, and NVIDIA is the primary beneficiary as the dominant merchant silicon provider.3
- CUDA moat. Nvidia's CUDA software platform has 17+ years of development and is deeply embedded across enterprise AI workloads, making it prohibitively expensive for customers to switch ecosystems without rewriting applications.4
- Strong guidance. Management guided Q2 FY27 revenue of $91.0B (±2%), implying continued sequential growth, with gross margins expected at ~75% — well above semiconductor industry averages.5
Bear Thesis
- China export control risk. NVIDIA incurred a $4.5B charge in Q1 FY26 from H20 excess inventory and purchase obligations after China export restrictions tightened. The company is "not assuming any Data Center compute revenue from China" in its outlook.6
- Hyperscaler custom silicon. Amazon, Google, Meta, and Microsoft are all aggressively ramping custom AI accelerators (Trainium2, TPU, MTIA, Maia) — over 60% of AWS ML instances already run on some form of Amazon silicon.7
- Valuation premium. At ~42× trailing earnings and ~$5.1T market cap, NVDA must sustain hypergrowth to justify its multiple — any guidance miss or demand plateau could trigger a sharp de-rating.
- Customer concentration. The top four hyperscalers account for a disproportionate share of data center revenue, creating single-customer concentration risk that could surface if any major buyer pivots to in-house silicon or AMD.8
- Chinese domestic competition. Huawei's Ascend chips operate at 60–70% of H200 capability, and China is targeting semiconductor self-reliance by 2027 — if achieved, NVIDIA's China market (historically >55% share there) could collapse.9
Executive Summary
NVIDIA Corporation is the world's dominant designer of graphics processing units (GPUs) and AI accelerator systems. Founded in 1993 and headquartered in Santa Clara, California,10 the company has transformed from a gaming-chip maker into the central infrastructure vendor powering the global AI buildout — a position that has made it one of the most valuable companies in history.
The fundamentals are exceptional by any measure. In fiscal year 2026 (ended January 2026), NVIDIA reported revenue of $215.9 billion, up 65% year-over-year from $130.5 billion in FY25,11 with net income of $120.1 billion12 and a full-year gross margin of 71.1% (GAAP).13 The Blackwell GPU architecture — shipping in GB200 and GB300 NVL72 rack-scale systems — is sold out through at least mid-2026, with hyperscaler order backlogs running into the millions of units.2 Q1 FY27 revenue (reported May 20, 2026) printed at $81.6 billion, +85% year-over-year,1 and management guided Q2 FY27 to $91.0 billion.5 The balance sheet is fortress-grade: $50.3 billion in cash and marketable securities against $8.5 billion in total debt.14
The bull case rests on three pillars: (1) the agentic AI wave is early and compute demand will continue compounding as inference workloads scale; (2) CUDA's 17+ years of ecosystem lock-in makes switching costs effectively prohibitive for most enterprise customers;4 and (3) hyperscaler AI capex, guided at $380B+ for 2026 across the four largest cloud providers,3 flows disproportionately to NVIDIA. The bear case requires either (a) a meaningful deceleration in AI buildout spending, (b) rapid maturation of hyperscaler custom silicon eroding merchant GPU share, or (c) China export controls turning from a one-quarter charge into a structural revenue ceiling — all plausible but not yet dominant forces.
Company History & Leadership
NVIDIA was founded on January 25, 1993, at a Denny's restaurant in San Jose, California, by Jensen Huang, Chris Malachowsky, and Curtis Priem with $40,000 in starting capital and a vision to solve 3D graphics for the PC market.10 The trio initially called their company NVision before discovering the name was taken, and renamed it NVIDIA. Jensen Huang has served as president and CEO from inception to the present day.
The company went public on the Nasdaq in January 1999.10 Its invention of the GPU — a term it coined — ignited the PC gaming market and, decades later, became the foundational compute substrate for modern deep learning. Today NVIDIA is one of the most valuable companies in the world, headquartered in Santa Clara, California, with a market capitalization exceeding $5 trillion.
Leadership
| Name | Title | Tenure | Background |
|---|---|---|---|
| Jensen Huang | President & CEO | 1993 – present | Co-founder; previously at AMD and LSI Logic; holds BS EE (Oregon State), MS EE (Stanford). |
| Colette M. Kress | EVP & CFO | Sept 2013 – present | Previously CFO roles at Cisco and Microsoft; No. 49 on Fortune's 2026 Most Powerful Women list.18 |
| Ajay K. Puri | EVP, Worldwide Field Operations | 2005 – present | Oversees global sales and go-to-market execution.18 |
| Timothy S. Teter | EVP & General Counsel | 2015 – present | Oversees legal, compliance, and IP strategy.18 |
Business Model & Unit Economics
NVIDIA designs semiconductors and full-stack computing platforms. It is a fabless company — it designs chips and outsources manufacturing primarily to TSMC — then sells complete systems comprising silicon, networking (InfiniBand via Mellanox), software (CUDA, cuDNN, TensorRT), and increasingly managed cloud services.
Revenue is reported in four segments. Data Center is overwhelmingly dominant: in FY2026, it generated $193.7 billion, representing 89.7% of total company revenue of $215.9 billion.19 Within Data Center, management has introduced a further breakdown between Hyperscale (large cloud providers buying at rack-scale) and ACIE (AI Clouds, Industrial, and Enterprise). In Q1 FY27, hyperscale revenue was $37.9 billion and ACIE was $37.4 billion — nearly equal splits at $75.2 billion combined.1
Gaming is NVIDIA's heritage segment: in FY2026 it produced $16.0 billion (+41% YoY), a record, driven by the GeForce RTX 50-series.19 Professional Visualization (workstation GPUs under the Quadro/RTX brand) contributed $3.2 billion (1.5%), and Automotive (DRIVE platform for autonomous vehicles) added $2.3 billion (1.1%), both growing but small in context of the AI data center wave.19
The unit economics are exceptional. Full-year gross margin was 71.1% (GAAP) in FY2026.13 NVIDIA prices its H100/H200/B200 GPUs at premiums — H100 SXM5s historically sold for $25,000–$40,000 per unit — because performance superiority and software lock-in suppress price sensitivity. The CUDA ecosystem is the deepest moat: 17+ years of developer tooling, libraries (cuDNN, cuBLAS, TensorRT), and millions of trained users mean that every frontier AI model — from Anthropic's Claude to OpenAI's GPT series to Meta's Llama — has been trained on NVIDIA hardware and tooling, creating a self-reinforcing cycle.4
NVIDIA is transitioning to a platform model it calls "AI Factory": selling not just GPUs but full-system reference designs (NVL72 racks), software stacks (NVIDIA AI Enterprise, NIM inference microservices), and DGX Cloud managed services, expanding recurring software revenue alongside hardware cycles.
Revenue Segment Summary (FY2026)
| Segment | FY2026 Revenue | % of Total | YoY Growth |
|---|---|---|---|
| Data Center | $193.7B | 89.7% | — |
| Gaming | $16.0B | 7.4% | +41% |
| Professional Visualization | $3.2B | 1.5% | — |
| Automotive | $2.3B | 1.1% | — |
| Total | $215.9B | 100% | +65% |
Source: NVIDIA FY2026 earnings release.11
Price History & Technicals
| Metric | Value | Note |
|---|---|---|
| Last Close (June 18, 2026) | $210.69 | +2.95% on the day20 |
| 52-Week High | $236.54 | Intraday; ATH closing price $235.47 (May 14, 2026)20 |
| 52-Week Low | $142.03 | 52-week low20 |
| 52-Week Return | +46.47% | Price appreciation over trailing year20 |
| All-Time High Close | $235.47 | May 14, 202621 |
| Market Cap (approx.) | ~$5.1T | Based on last close and ~24.3B diluted shares |
| NASDAQ IPO Date | January 1999 | Listed on Nasdaq10 |
NVIDIA's stock traversed a remarkable arc in the 52 weeks through June 2026. The share price started the period near its 52-week low of $142.03, reflecting a sharp drawdown from what had been a prior high near $153 in January 2025 — itself followed by a decline to roughly $105 amid tariff uncertainty and broader risk-off sentiment.21 The recovery was driven by sequential blowout earnings: each quarterly print from FY26 through Q1 FY27 surpassed consensus estimates, with data center revenue growth accelerating from the 60s-percent range to 92% YoY in Q1 FY27.
NVIDIA reached a new all-time closing high of $235.47 on May 14, 2026 — just ahead of the Q1 FY27 earnings report on May 20, 2026.21 The stock slipped slightly post-earnings despite the beat, a pattern common with priced-in perfection at high-multiple growth names, before recovering back through the $210 range by June 18.
Over the past year, NVDA returned +46.47%,20 substantially outperforming the S&P 500. From a technical standpoint, the stock is consolidating below its ATH, with strong support from the consensus analyst price target of $298.9322 providing a fundamental backstop well above current levels. The 52-week range of $142.03–$236.54 reflects the market's continued recalibration of the AI infrastructure demand cycle.
Financial Statements & Guidance
Income Statement Highlights
| Metric | FY2024 | FY2025 | FY2026 | Q1 FY2027 |
|---|---|---|---|---|
| Revenue | $60.9B | $130.5B | $215.9B | $81.6B |
| YoY Growth | +122% | +114% | +65% | +85% |
| GAAP Gross Margin | 72.7% | 74.6% | 71.1% | 60.5%* |
| Net Income (GAAP) | $29.8B | $72.9B | $120.1B | — |
| EPS (diluted, GAAP) | $1.19 | $2.94 | $4.93 | $0.76 |
| EPS (diluted, non-GAAP) | — | — | — | $0.81 |
*Q1 FY27 GAAP gross margin of 60.5% reflects the $4.5B H20 inventory charge taken in Q1 FY26 distorting comparative base; ex-charge, non-GAAP GM was 61.0%. FY2026 and prior sourced from NVIDIA earnings releases.111
Balance Sheet (as of Q1 FY27, ~April 2026)
NVIDIA's balance sheet is exceptionally strong. Cash, cash equivalents, and marketable debt securities stood at $50.3 billion as of the end of Q1 FY27.14 Total debt was approximately $8.5 billion, giving a net cash position of roughly $41.8 billion. Total shareholders' equity was $195.5 billion,14 and total assets were $259.5 billion against total liabilities of $64.0 billion.14 The debt-to-equity ratio of approximately 4.3% is minimal for a company of NVIDIA's scale, and the cash position more than covers all long-term debt obligations.14
Forward Guidance
For Q2 FY27, NVIDIA guided revenue of $91.0 billion ±2%, with GAAP gross margins expected at 74.9% and non-GAAP at 75.0% (±50 bps).5 The company stated it is "not assuming any Data Center compute revenue from China" in its Q2 FY27 outlook,6 making the guidance conservative relative to a China-resumption scenario. GAAP and non-GAAP tax rates for the full year are expected to be 16.0%–18.0% excluding discrete items.5 Analyst consensus projects revenue growth of approximately 23% per annum on average over the next three years.23
Sell-Side View
Wall Street is overwhelmingly bullish on NVIDIA. According to S&P Global's consensus of 62 analysts, NVDA carries a "Strong Buy" rating with an average price target of $298.93 — implying approximately 42% upside from the June 18, 2026, close of $210.69.22 Of 61 analysts with ratings on record, 58 rate the stock Buy, 2 Hold, and 1 Sell.22
Selected Analyst Ratings & Price Targets
| Firm | Rating | Price Target | Date / Note |
|---|---|---|---|
| Baird | Buy | $500 | May 21, 2026 — highest known target22 |
| S&P Global Consensus | Strong Buy | $298.93 | Average of 62 analysts22 |
| Benzinga Consensus | Strong Buy | $309.13 | Average of 32 analysts24 |
| Deutsche Bank | Hold | $215 | November 20, 2025 — low of range22 |
| Median (22 analysts) | Bullish | $284.00 | Range $180–$50024 |
Price Target Dispersion
The target range of $180 to $500 reflects genuine uncertainty about the magnitude and durability of the AI infrastructure buildout.24 The bull case ($400–$500 targets) assumes the agentic AI wave drives compute demand for many more years at high rates; the bear/cautious case ($180–$215) prices in a normalization of hyperscaler capex, slower-than-expected Blackwell ramp, or a meaningful China revenue ceiling. Deutsche Bank's $215 target from November 2025 sits just above the current price, making it effectively the street's lone skeptic.22
The average 1-year price forecast across 68 analysts implies a +41.88% move from current levels,24 consistent with the view that NVIDIA's earnings power continues to compound faster than the stock multiple has expanded. The overwhelming buy-side skew (58:2:1 Buy:Hold:Sell) is historically unusual even for mega-cap tech and reflects consensus belief that the Blackwell-to-Rubin product cycle sustains growth into FY28 and beyond.
Partnerships, Customers & Suppliers
One of NVIDIA's largest customers by volume. Azure is deploying Blackwell-based infrastructure at scale, and Microsoft guided ~$90–100B in 2026 AI capex — a significant share flowing through NVIDIA hardware.3
Google has offered a preview of its Nvidia Blackwell GB200 NVL rack deployments and committed ~$90–100B in 2026 AI capex. Google was also first among hyperscalers to design its own AI accelerator (TPU, 2015), making it both a customer and a competitive threat.7
AWS leads hyperscaler AI capex at approximately $118B in 2026 guidance. While over 60% of AWS ML instances run on Amazon silicon (Trainium, Inferentia), AWS continues to be a major NVIDIA GPU buyer for third-party workloads.7
Meta is guiding 2026 capex of roughly $70–72B, primarily for AI infrastructure. Meta's CEO noted the company is "exploring multiple silicon providers to optimize for different workload types," indicating partial reliance on NVIDIA alongside its MTIA custom chip program.7
NVIDIA is fabless and relies almost entirely on TSMC for advanced-node chip manufacturing. Blackwell chips are built on TSMC's 4NP process, making TSMC supply allocation a critical operational dependency.
OpenAI's GPT series and infrastructure run on NVIDIA hardware. Jensen Huang cited OpenAI as one of several frontier AI model providers running exclusively on the "only platform that runs every frontier AI model."15
Anthropic (maker of Claude) was cited by Jensen Huang on the Q1 FY27 earnings call as one of the frontier AI providers running on NVIDIA's platform.15
AMD's Instinct MI300X/MI455 lines are the strongest merchant-chip alternative to NVIDIA. AMD launched the MI455 and Helios data center system at CES 2026 with a partnership with OpenAI — the first serious sign of a hyperscaler co-developing on non-NVIDIA silicon for training workloads.25
NVIDIA's partnership strategy is designed to lock in the full AI compute stack at every layer: hardware (GPUs/NVLink/InfiniBand), software (CUDA/NCCL/cuDNN/TensorRT), reference designs (DGX, HGX, MGX), and cloud (DGX Cloud). The scale of hyperscaler capex spending in 2026 — $380B+ combined across the Big Four3 — makes NVIDIA less a supplier than an indispensable infrastructure layer: the picks-and-shovels provider for the AI gold rush.
Competition
NVIDIA holds approximately 80%+ market share in data center AI accelerators as of Q1 2026,26 but the competitive landscape is intensifying on multiple fronts: merchant silicon rivals (AMD), hyperscaler-designed custom chips (Google TPU, Amazon Trainium, Microsoft Maia, Meta MTIA), and domestic Chinese alternatives (Huawei Ascend). No single rival has yet mounted a material threat to NVIDIA's training-workload dominance, but the inference and edge markets are increasingly contested.
| Competitor | Type | Key Product | Positioning vs NVIDIA |
|---|---|---|---|
| AMD | Public, Merchant | Instinct MI300X, MI455 | MI300X offers 192GB HBM3 (vs H100's 80GB) at competitive pricing; MI455 + Helios system targets hyperscale training. AMD-OpenAI partnership announced at CES 2026.25 |
| Intel | Public, IDM | Gaudi 3 AI Accelerators | Positioned as cost-effective alternative; targets CPU-based and integrated AI workloads; overall AI accelerator traction has been limited relative to NVIDIA and AMD.26 |
| Google (TPU) | Public, Custom Silicon | TPU v5p / v6 | Google was first hyperscaler with custom AI ASIC (2015); TPUs now power a significant portion of Google's internal AI training and inference. Not available externally as merchant silicon.7 |
| Amazon (AWS) | Public, Custom Silicon | Trainium2, Inferentia3 | Over 60% of AWS ML instances run on Amazon silicon. Trainium2 targets training at lower cost; Inferentia targets inference.7 |
| Microsoft | Public, Custom Silicon | Azure Maia 100 | Deployed for internal Microsoft AI workloads (Azure, Copilot, OpenAI partnership) — primarily targeting inference cost reduction.7 |
| Huawei (Ascend) | Private (China), Custom | Ascend 910B/C | Best domestic Chinese chip at 60–70% of H200 capability; produced in hundreds of thousands vs NVIDIA's millions; primarily a China-market player.9 |
NVIDIA's primary moat remains the CUDA software ecosystem. With 17+ years of development,4 CUDA is effectively the operating system of AI computing — models, frameworks (PyTorch, TensorFlow, JAX), and developer workflows are deeply intertwined with CUDA semantics. ROCm (AMD's CUDA alternative) has improved significantly but enterprise adoption of AMD remains primarily in cost-sensitive workloads where CUDA compatibility can be retrofitted.
The more structural risk is not AMD but custom silicon. Amazon, Google, and Microsoft collectively represent a large portion of NVIDIA's data center revenue. As their in-house chips mature, they can direct marginal workloads to custom silicon — not necessarily displacing NVIDIA entirely, but capping its share of any individual hyperscaler's AI spend. The key question for bulls is whether total AI compute demand grows fast enough to absorb both NVIDIA's growth and the custom silicon ramp simultaneously — a scenario the current capex trajectory ($380B+ in 2026) appears to support.3
In China, Huawei's Ascend chips are the principal domestic alternative, but at 60–70% of H200 capability and produced in much smaller volumes,9 they are not yet a like-for-like substitute for frontier AI training. The more meaningful near-term China competitive risk is not technical inferiority but political: a self-imposed US export restriction that gifts China's AI ecosystem time to mature on domestic chips.
Risks & the Bear Case
US export restrictions on AI chips to China are volatile and unpredictable. NVIDIA incurred a $4.5B H20 inventory charge in Q1 FY26 and is not assuming any China DC compute revenue in its Q2 FY27 guidance. A permanent China revenue ceiling would remove a large TAM.6
Amazon, Google, Microsoft, and Meta are all building custom AI accelerators. If custom silicon captures 20–30% of hyperscaler AI workloads over three years, NVIDIA's revenue growth could decelerate materially even amid rising overall capex.7
The top four hyperscalers account for a disproportionate share of NVIDIA's data center revenue. Loss of, or reduced orders from, any single hyperscaler would be a meaningful revenue shock. NVIDIA itself has signaled a desire to reduce this dependency by growing enterprise/ACIE.8
At ~$5.1T market cap and ~42× trailing GAAP EPS, NVDA is priced for sustained hypergrowth. Any miss on guidance, a deceleration in AI capex, or multiple compression in the broader market could cause a sharp de-rating given the limited margin of safety at current prices.
NVIDIA is nearly entirely dependent on TSMC for manufacturing. Any disruption to TSMC — geopolitical (Taiwan Strait tensions), natural disaster, or allocation competition with Apple/AMD/Qualcomm — could constrain chip supply in ways NVIDIA cannot control.
US–China trade tensions have already materialized into revenue impact (H20 charge).6 Escalation — whether new export restrictions, Chinese tariffs, or Taiwan Strait instability — poses tail-risk to both revenue and supply chain simultaneously.
The current AI infrastructure buildout is the primary driver of NVIDIA's revenue. If enterprise AI monetization disappoints, if hyperscalers overbuild (as happened in cloud in 2022), or if a next-generation algorithmic breakthrough reduces compute requirements, capex could decelerate sharply.
NVIDIA's dominant market position in AI accelerators is attracting regulatory scrutiny in the US and EU. The FTC blocked the Arm acquisition; future M&A may face challenges. CUDA's effective monopoly on AI training infrastructure could also invite antitrust attention.
Bear Thesis Deep-Dive
The steelmanned bear case on NVIDIA is not that the company is poorly run or the technology inferior — it is a timing and valuation argument. At ~$5.1 trillion market cap, NVIDIA is priced as if the AI compute buildout (a) continues at current rates for many years, (b) remains majority-served by merchant silicon rather than custom chips, and (c) faces no structural China revenue ceiling. Each of these assumptions is contestable.
The hyperscaler custom silicon build is the most underappreciated risk. Amazon, Google, Microsoft, and Meta have collectively committed hundreds of billions in capex to build their AI infrastructure — and all four are simultaneously accelerating custom chip programs. Google's TPU has been deployed internally since 2015 and available externally since 2018;7 Amazon reports over 60% of its AWS ML instances now running on Amazon silicon;7 Meta explicitly stated it is "exploring multiple silicon providers."7 If each hyperscaler shifts even a quarter of marginal AI spending to custom silicon, the impact on NVIDIA's revenue growth rate could be significant — even if total AI capex continues to grow.
On China: NVIDIA is not merely facing a one-quarter disruption. The company is operating with zero assumed China data center revenue in its forward guidance,6 and US–China semiconductor tensions show no signs of structural resolution. Huawei's Ascend chips, currently at 60–70% of H200 capability,9 are improving — and China's government-backed self-reliance push could close that gap over 2–3 years. China historically represented the world's fastest-growing AI market; NVIDIA's permanent exclusion from it represents a meaningful TAM constraint.
The valuation math: at $210.69, NVDA trades at ~42× FY26 GAAP EPS of $4.93.12 If revenue growth decelerates to 20–25% in FY28+ (still exceptional by any standard), and the multiple compresses from 42× to 25–30× (still elevated vs. the S&P 500), the stock could be flat-to-down over a 2–3 year horizon even with continued earnings growth. The bear does not need NVIDIA to fail — only for growth to decelerate faster than the market currently prices.
Catalysts — Recent & Upcoming
Recent Catalysts (March – June 2026)
Upcoming Watch List
| Event | Expected Timing | Why It Matters |
|---|---|---|
| Q2 FY27 Earnings | ~August 2026 | First full quarter with $91B+ guidance; key test of whether Blackwell Ultra demand sustains sequential growth; China revenue resumption optionality. |
| Vera Rubin (Rubin) Launch | H2 2026 (expected) | Next-gen architecture officially showcased at GTC 2026; shipments projected to begin H2 2026. 5× Blackwell inference performance would extend NVIDIA's roadmap lead.17 |
| China AI Chip Policy | Ongoing | US policy on H200/equivalent chip exports to China is in flux. Any easing would be a meaningful revenue upside; further tightening would extend the China revenue drought. |
| Hyperscaler Q2 Earnings & Capex Updates | July–August 2026 | Amazon, Google, Microsoft, and Meta Q2 results will update 2026 AI capex guidance — the primary demand signal for NVIDIA hardware orders. |
References
- NVIDIA Announces Financial Results for First Quarter Fiscal 2027 NVIDIA Newsroom · May 20, 2026
- Nvidia's Blackwell Dynasty: B200 and GB200 Sold Out Through Mid-2026 as Backlog Hits 3.6 Million Units Financial Content / WRAL · December 29, 2025
- AI Hyperscaler Capex 2026: Microsoft, Google, Meta, Amazon $380B+ Compared by Company Value Add VC · 2026
- AI Chip Wars 2026: NVIDIA vs AMD vs Intel for Developers CoderCops · 2026
- NVIDIA CORP — Form 8-K Q1 FY27 Press Release SEC EDGAR / NVIDIA · May 2026
- NVIDIA Announces Financial Results for First Quarter Fiscal 2026 NVIDIA Newsroom · May 2025
- Inside the Custom AI Chip Race: Google, AWS, Microsoft, Meta, OpenAI Hashrate Index · 2026
- NVIDIA Customer Concentration: A Big 4 Earnings Preview Daloopa · 2026
- NVIDIA's Crossroads in China: Can Export Controls Halt Its AI Dominance? AInvest · 2026
- The Story of Jensen Huang and Nvidia Quartr Insights · 2024
- NVIDIA Announces Financial Results for Fourth Quarter and Fiscal 2026 NVIDIA Newsroom · February 2026
- NVIDIA Reports Q4 FY2026 Earnings: Data Center and ProViz Drive Revenue Records ServeTheHome · February 2026
- NVIDIA CORP — Form 8-K Q4 FY26 Press Release SEC EDGAR / NVIDIA · February 2026
- NVIDIA (NVDA) Balance Sheet Stock Analysis · June 2026
- Nvidia earnings takeaways: Data center revenue nearly doubles, report is strong but stock slides CNBC · May 20, 2026
- NVIDIA Blackwell Platform Arrives to Power a New Era of Computing NVIDIA Newsroom · 2024
- The AI Chip Showdown at CES 2026: Nvidia, AMD, and Intel's Strategic Moves for Dominance AInvest · January 2026
- NVIDIA C-Suite Executive Leadership Team [2026] DigitalDefynd · 2026
- NVIDIA Corporation Revenue Breakdown By Segment Bullfincher · 2026
- NVDA Stock Quote Price and Forecast CNN Markets · June 2026
- NVIDIA — 27 Year Stock Price History | NVDA MacroTrends · June 2026
- NVIDIA Analyst Ratings and Price Targets | NASDAQ:NVDA Benzinga · June 2026
- NVIDIA (NasdaqGS:NVDA) Stock Forecast & Analyst Predictions Simply Wall St · 2026
- NVIDIA (NVDA) Stock Forecast & Analyst Price Targets Stock Analysis · June 2026
- CES 2026: AMD Launches MI455 and Helios System with OpenAI Partnership AInvest · January 2026
- The AI Chip Market Explosion: Key Stats on Nvidia, AMD, and Intel's AI Dominance PatentPC · 2026
- Nvidia Stock After Earnings: Nvidia Reports $81.6B Revenue, Raises Dividend 25x IndMoney · May 2026
- NVIDIA's 'Blackwell Ultra' GB300 AI Servers to Lead the AI Infrastructure Race in 2026 WCCFTech · 2025/2026