Nvidia Corp (NVDA) Stock Analysis and Metrics

Nvidia

NASDAQ: NVDA

$211.95 ▲ +8.42 (+4.14%)

At close: Jul 14, 2026 · 2:17 PM UTC

Financial Ratios

Market Cap4,798.43 Bn

P/E30.07

P/S18.93

Div. Yield0.00

ROIC (Qtr)0.01

Total Debt (Qtr)8.47 Bn

Revenue Growth (1y) (Qtr)85.23

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About

NVIDIA pioneered accelerated computing to help solve the most challenging computational problems. The company is now a data center scale AI infrastructure company reshaping all industries. Its technology stack includes the foundational NVIDIA CUDA development platform that runs on all NVIDIA GPUs, as well as hundreds of domain-specific software libraries, frameworks, algorithms, software development kits, or SDKs, and application programming interfaces, or APIs. This deep and broad software stack accelerates the performance and facilitates the deployment of NVIDIA accelerated computing for computationally intensive workloads such as artificial intelligence, or AI, model training and inference, data analytics, scientific computing, robotics, and 3D graphics, with vertical-specific optimizations to address industries ranging from healthcare and telecom to automotive and manufacturing.

NVIDIA generates revenue through the sale of its graphics processing units, or GPUs, and related hardware, software, and services. The company’s products include data center compute and networking infrastructure, gaming GPUs, professional visualization solutions, and automotive platforms for autonomous driving. Revenue is derived from direct sales to customers and through partnerships with original equipment manufacturers, or OEMs, original device manufacturers, or ODMs, system integrators, and distributors. NVIDIA also earns revenue from software offerings such as NVIDIA AI Enterprise, which provides a comprehensive suite of enterprise-grade AI software, and from cloud gaming services like GeForce NOW.

The company operates through the following segments: Compute & Networking and Graphics.

• Compute & Networking: This segment includes NVIDIA's data center accelerated computing and networking platforms, AI solutions and software, and automotive platforms for autonomous and electric vehicle solutions including software. It encompasses data center GPUs such as the Hopper and Blackwell architectures, CPUs like Grace, data processing units or DPUs, and networking products including NVLink and InfiniBand solutions. The segment also features software stacks such as CUDA, CUDA-X, AI frameworks, and NVIDIA AI Enterprise, which enables enterprise AI workloads.

• Graphics: This segment includes GeForce GPUs for gaming and personal computers, and Quadro/NVIDIA RTX GPUs for enterprise workstation graphics. Key product lines under this segment are the GeForce RTX series for gaming, which feature real-time ray tracing and DLSS technology, and the RTX PRO series for professional applications in design, engineering, and content creation. The segment also supports GeForce NOW, NVIDIA's cloud gaming service that streams games to various devices.

NVIDIA holds a dominant position in the global graphics processing unit market, particularly in data center AI and gaming segments, where it competes primarily with Advanced Micro Devices, or AMD, and Intel Corporation. The company's competitive advantage stems from its full-stack approach integrating hardware, software, and ecosystem support, including the CUDA platform used by over 7.5 million developers worldwide. NVIDIA's leadership is reinforced by its innovation in accelerated computing, with its GPUs powering more than half of the world's top supercomputers and enabling advancements in AI, scientific research, and industrial automation.

NVIDIA serves a diverse customer base including major cloud service providers such as Amazon Web Services, Microsoft Azure, and Google Cloud; leading AI model makers; enterprises across healthcare, financial services, manufacturing, and retail sectors; original equipment manufacturers like Dell Technologies and Hewlett Packard Enterprise; and system integrators. The company also supplies its products to automotive manufacturers for autonomous driving systems, video game developers, and professional users in design and engineering fields who rely on its professional visualization solutions.

Investment Thesis

▲ Bull case

NVIDIA's transition to a segmented reporting framework—separating hyperscale and ACIE (AI Cloud, Industrial, Enterprise) within data center—reveals a hidden catalyst: ACIE revenue grew 31% quarter-over-quarter in Q1 FY27, significantly outpacing hyperscale's 12% growth, driven by AI cloud revenue that more than tripled year-over-year. This segment, which includes sovereign AI deployments across nearly 40 countries representing $50 trillion in GDP and expanding partner data centers exceeding 10MW (now surpassing 80 sites), is being underestimated by the market as merely a subset of data center. In reality, ACIE represents the long-term structural shift where AI adoption moves beyond hyperscalers into diverse industries and geographies, leveraging NVIDIA's extreme codesign advantage to deliver lowest token cost and highest ROI. The company's visibility into nearly $20 billion in total CPU revenue this year from Vera, a brand-new $200 billion TAM never previously addressed, further de-risks dependence on GPU-centric growth and positions NVIDIA to capture value across the entire AI stack—from inference orchestration on CPUs to token generation on GPUs—making its addressable market far broader than current hyperscaler CapEx estimates suggest.
The DG Matrix Interport™ solid-state transformer (SST) platform integration with NVIDIA's MGX modular rack architecture is an underappreciated catalyst solving a critical bottleneck in AI factory deployment: power infrastructure. As AI workloads drive unprecedented electricity demand, the Interport SST delivers native 800 VDC power with up to 98.5% conversion efficiency, rack-proximate power, and dynamic GPU pulse-load response—directly addressing energy losses and physical footprint constraints in high-density AI environments. This collaboration, validated through yearlong testing with NVIDIA on energy storage solutions for dynamic GPU load response, enables faster deployment, lower energy costs, and flexible integration of all energy sources at scale. By future-proofing deployments for up to 1,500 VDC and enabling behind-the-meter power aggregation from multiple AC/DC sources, NVIDIA's MGX ecosystem—now enhanced by DG Matrix—reduces a key constraint on AI infrastructure rollout, particularly for hyperscale, neocloud, and sovereign deployments in APAC and beyond, where power density and efficiency are paramount to sustaining the $3–4 trillion annual AI infrastructure spending opportunity by decade-end.
NVIDIA's Vera CPU is not merely an incremental product but a strategic inflection point enabling agentic AI at scale, with the company guiding to $20 billion in CPU revenue this year alone—a figure derived from standalone Vera sales, not bundled with Rubin GPUs. Unlike past CPU efforts, Vera is purpose-built for agentic workloads: delivering up to 1.5x faster performance per core, 2x performance per watt, and 4x density per rack versus x86 alternatives, while enabling end-to-end confidential computing via Vera Rubin. This opens a $200 billion TAM in CPU markets never previously served, with every major hyperscaler and system maker partnering on deployment. Crucially, as agentic AI shifts orchestration and tool use to CPUs (e.g., browser use, code compilation, simulator runs), and billions of future agents will require PC-like compute for sub-agent spawning and inference, NVIDIA's Vera addresses a structural demand shift where CPUs become essential companions to GPUs—not competitors—thereby expanding NVIDIA's revenue per AI factory beyond GPU sales alone and reducing reliance on cyclical data center CapEx.
The NVIDIA DDNs partnership announcement reveals a hidden structural advantage in enterprise AI factory operationalization: DDN's AI-native data intelligence platform, powered by NVIDIA accelerated computing, delivers real-time observability, policy-based control, secure multi-tenant isolation, and AI-native data orchestration—critical for moving agentic AI from pilot to production. By integrating NVIDIA Vera BlueField-4 STX architecture and DOCA security framework, the solution enables inline security enforcement, memory observability, and zero-trust controls directly within the AI data path, eliminating performance bottlenecks while maximizing GPU utilization. This addresses the unspoken challenge enterprises face: autonomous AI agents continuously retrieving, generating, reasoning over, and acting on data in real time create unprecedented demands for governance and security that traditional host-based defenses cannot meet. NVIDIA's end-to-end stack—from Vera CPUs and Rubin GPUs to BlueField-4 STX and DOCA—provides a unique, secure-by-design infrastructure that reduces operational complexity and accelerates ROI, positioning the company to capture value in the $50–80 trillion industrial and enterprise AI opportunity that hyperscalers alone cannot serve.

NVIDIA's transition to a segmented reporting framework—separating hyperscale and ACIE (AI Cloud, Industrial, Enterprise) within data center—reveals a hidden catalyst: ACIE revenue grew 31% quarter-over-quarter in Q1 FY27, significantly outpacing hyperscale's 12% growth, driven by AI cloud revenue that more than tripled year-over-year. This segment, which includes sovereign AI deployments across nearly 40 countries representing $50 trillion in GDP and expanding partner data centers exceeding 10MW (now surpassing 80 sites), is being underestimated by the market as merely a subset of data center. In reality, ACIE represents the long-term structural shift where AI adoption moves beyond hyperscalers into diverse industries and geographies, leveraging NVIDIA's extreme codesign advantage to deliver lowest token cost and highest ROI. The company's visibility into nearly $20 billion in total CPU revenue this year from Vera, a brand-new $200 billion TAM never previously addressed, further de-risks dependence on GPU-centric growth and positions NVIDIA to capture value across the entire AI stack—from inference orchestration on CPUs to token generation on GPUs—making its addressable market far broader than current hyperscaler CapEx estimates suggest.
The DG Matrix Interport™ solid-state transformer (SST) platform integration with NVIDIA's MGX modular rack architecture is an underappreciated catalyst solving a critical bottleneck in AI factory deployment: power infrastructure. As AI workloads drive unprecedented electricity demand, the Interport SST delivers native 800 VDC power with up to 98.5% conversion efficiency, rack-proximate power, and dynamic GPU pulse-load response—directly addressing energy losses and physical footprint constraints in high-density AI environments. This collaboration, validated through yearlong testing with NVIDIA on energy storage solutions for dynamic GPU load response, enables faster deployment, lower energy costs, and flexible integration of all energy sources at scale. By future-proofing deployments for up to 1,500 VDC and enabling behind-the-meter power aggregation from multiple AC/DC sources, NVIDIA's MGX ecosystem—now enhanced by DG Matrix—reduces a key constraint on AI infrastructure rollout, particularly for hyperscale, neocloud, and sovereign deployments in APAC and beyond, where power density and efficiency are paramount to sustaining the $3–4 trillion annual AI infrastructure spending opportunity by decade-end.
NVIDIA's Vera CPU is not merely an incremental product but a strategic inflection point enabling agentic AI at scale, with the company guiding to $20 billion in CPU revenue this year alone—a figure derived from standalone Vera sales, not bundled with Rubin GPUs. Unlike past CPU efforts, Vera is purpose-built for agentic workloads: delivering up to 1.5x faster performance per core, 2x performance per watt, and 4x density per rack versus x86 alternatives, while enabling end-to-end confidential computing via Vera Rubin. This opens a $200 billion TAM in CPU markets never previously served, with every major hyperscaler and system maker partnering on deployment. Crucially, as agentic AI shifts orchestration and tool use to CPUs (e.g., browser use, code compilation, simulator runs), and billions of future agents will require PC-like compute for sub-agent spawning and inference, NVIDIA's Vera addresses a structural demand shift where CPUs become essential companions to GPUs—not competitors—thereby expanding NVIDIA's revenue per AI factory beyond GPU sales alone and reducing reliance on cyclical data center CapEx.
The NVIDIA DDNs partnership announcement reveals a hidden structural advantage in enterprise AI factory operationalization: DDN's AI-native data intelligence platform, powered by NVIDIA accelerated computing, delivers real-time observability, policy-based control, secure multi-tenant isolation, and AI-native data orchestration—critical for moving agentic AI from pilot to production. By integrating NVIDIA Vera BlueField-4 STX architecture and DOCA security framework, the solution enables inline security enforcement, memory observability, and zero-trust controls directly within the AI data path, eliminating performance bottlenecks while maximizing GPU utilization. This addresses the unspoken challenge enterprises face: autonomous AI agents continuously retrieving, generating, reasoning over, and acting on data in real time create unprecedented demands for governance and security that traditional host-based defenses cannot meet. NVIDIA's end-to-end stack—from Vera CPUs and Rubin GPUs to BlueField-4 STX and DOCA—provides a unique, secure-by-design infrastructure that reduces operational complexity and accelerates ROI, positioning the company to capture value in the $50–80 trillion industrial and enterprise AI opportunity that hyperscalers alone cannot serve.

▼ Bear case

NVIDIA's guidance for $1 trillion in Blackwell and Rubin revenue from 2025 through calendar 2027 excludes Vera CPUs, LPX, Rubin CPX, and other combinations, yet the company relies on this figure to underpin long-term optimism. However, the exclusion of Vera CPUs—despite guiding to $20 billion in annual CPU revenue this year—creates a material gap in the $1 trillion narrative, as the CPU opportunity is presented as incremental but not integrated into the core AI chip forecast. Worse, the company admitted it has yet to generate any revenue from H200 shipments to China despite approved licenses, and remains uncertain whether imports will be allowed, rendering its China data center compute outlook effectively zero. This inconsistency—promising a $200 billion CPU TAM including China while conceding zero near-term China GPU revenue—reveals a strategic overreach: NVIDIA is counting on CPU demand in a market where it cannot yet sell its flagship GPUs, exposing the Vera opportunity to the same geopolitical headwinds that have already crippled its China data center business, where revenue from mainland China and Hong Kong halved year-over-year in the latest quarter.
Despite highlighting Vera's advantages—1.5x faster performance per core, 2x performance per watt, and 4x density per rack—NVIDIA provided no concrete data on customer adoption rates, pricing elasticity, or competitive win rates against Intel and AMD in the CPU market, raising concerns that the $20 billion CPU revenue guidance is aspirational rather than grounded in near-term demand. The company acknowledged it will be supply constrained throughout VeraRubin's life, yet failed to address how this constraint interacts with the ramp of Vera standalone CPUs, Vera with CX9 for storage/security, and VeraRubin systems—all drawing from the same limited wafer capacity. Furthermore, the shift to agentic AI increasing CPU demand for orchestration and tool use is offset by NVIDIA's own efforts to accelerate world-class tools (compilers, databases) to run on GPUs via CUDA, which could reduce long-term CPU dependency. Without transparency on Vera's attach rate to Rubin GPUs or standalone CPU uptake in early access programs, the $200 billion CPU TAM remains a top-down estimate vulnerable to execution risk in a market where NVIDIA has no historical share.
NVIDIA's expansion in Taiwan—planning to spend $150 billion annually and build a campus for 4,000 employees by 2030—creates a concentration risk that contradicts its own supply chain resilience messaging. While Huang frames Taiwan as the 'epicenter of the AI revolution,' the company simultaneously acknowledges regulatory hurdles in selling to mainland China, where revenue has halved, and relies on TSMC for advanced chip manufacturing. This geographic concentration exposes NVIDIA to Taiwan-specific risks: natural disasters (earthquakes, typhoons), geopolitical flashpoints with China, and potential shifts in local policy toward AI data centers or nuclear power—precisely the reversal NVIDIA's investment might trigger, as noted by TriOrient Investments. The $150 billion annual outlay, exceeding a single quarter's revenue, locks in massive fixed commitments in a single region just as the company seeks to diversify beyond hyperscalers, yet provides no hedging strategy for supply chain disruption in Taiwan, undermining its claim of a resilient, global ecosystem.
The earnings call revealed evasiveness when questioned about the sustainability of NVIDIA's extreme codesign advantage in the face of rising competition in photonics and co-packaged optics (CPO). While NVIDIA celebrated joining the NVLink Fusion ecosystem with Ayar Labs' CPO technology and investing $6.5 billion in photonics since March, it failed to address critical bottlenecks: manufacturing yield on complex co-packaged optical assemblies remains low due to unforgiving alignment requirements, and rework is typically impossible when defects occur. As noted by Futurum Group analysts, large-scale photonics adoption is not expected until 2028+, meaning NVIDIA's near-term AI infrastructure scaling still relies on electrical copper interconnects—which consume more energy and create operational costs that photonics aims to solve. By investing heavily in long-term solutions without resolving near-term bandwidth and power constraints in current-generation racks (e.g., NVL72, Blackwell), NVIDIA risks overestimating the immediacy of its infrastructure advantages while underestimating the time required for supply chain maturity in optical technologies, leaving its near-term gross margin and throughput guidance vulnerable to persistent copper-related inefficiencies.

NVIDIA's guidance for $1 trillion in Blackwell and Rubin revenue from 2025 through calendar 2027 excludes Vera CPUs, LPX, Rubin CPX, and other combinations, yet the company relies on this figure to underpin long-term optimism. However, the exclusion of Vera CPUs—despite guiding to $20 billion in annual CPU revenue this year—creates a material gap in the $1 trillion narrative, as the CPU opportunity is presented as incremental but not integrated into the core AI chip forecast. Worse, the company admitted it has yet to generate any revenue from H200 shipments to China despite approved licenses, and remains uncertain whether imports will be allowed, rendering its China data center compute outlook effectively zero. This inconsistency—promising a $200 billion CPU TAM including China while conceding zero near-term China GPU revenue—reveals a strategic overreach: NVIDIA is counting on CPU demand in a market where it cannot yet sell its flagship GPUs, exposing the Vera opportunity to the same geopolitical headwinds that have already crippled its China data center business, where revenue from mainland China and Hong Kong halved year-over-year in the latest quarter.
Despite highlighting Vera's advantages—1.5x faster performance per core, 2x performance per watt, and 4x density per rack—NVIDIA provided no concrete data on customer adoption rates, pricing elasticity, or competitive win rates against Intel and AMD in the CPU market, raising concerns that the $20 billion CPU revenue guidance is aspirational rather than grounded in near-term demand. The company acknowledged it will be supply constrained throughout VeraRubin's life, yet failed to address how this constraint interacts with the ramp of Vera standalone CPUs, Vera with CX9 for storage/security, and VeraRubin systems—all drawing from the same limited wafer capacity. Furthermore, the shift to agentic AI increasing CPU demand for orchestration and tool use is offset by NVIDIA's own efforts to accelerate world-class tools (compilers, databases) to run on GPUs via CUDA, which could reduce long-term CPU dependency. Without transparency on Vera's attach rate to Rubin GPUs or standalone CPU uptake in early access programs, the $200 billion CPU TAM remains a top-down estimate vulnerable to execution risk in a market where NVIDIA has no historical share.
NVIDIA's expansion in Taiwan—planning to spend $150 billion annually and build a campus for 4,000 employees by 2030—creates a concentration risk that contradicts its own supply chain resilience messaging. While Huang frames Taiwan as the 'epicenter of the AI revolution,' the company simultaneously acknowledges regulatory hurdles in selling to mainland China, where revenue has halved, and relies on TSMC for advanced chip manufacturing. This geographic concentration exposes NVIDIA to Taiwan-specific risks: natural disasters (earthquakes, typhoons), geopolitical flashpoints with China, and potential shifts in local policy toward AI data centers or nuclear power—precisely the reversal NVIDIA's investment might trigger, as noted by TriOrient Investments. The $150 billion annual outlay, exceeding a single quarter's revenue, locks in massive fixed commitments in a single region just as the company seeks to diversify beyond hyperscalers, yet provides no hedging strategy for supply chain disruption in Taiwan, undermining its claim of a resilient, global ecosystem.
The earnings call revealed evasiveness when questioned about the sustainability of NVIDIA's extreme codesign advantage in the face of rising competition in photonics and co-packaged optics (CPO). While NVIDIA celebrated joining the NVLink Fusion ecosystem with Ayar Labs' CPO technology and investing $6.5 billion in photonics since March, it failed to address critical bottlenecks: manufacturing yield on complex co-packaged optical assemblies remains low due to unforgiving alignment requirements, and rework is typically impossible when defects occur. As noted by Futurum Group analysts, large-scale photonics adoption is not expected until 2028+, meaning NVIDIA's near-term AI infrastructure scaling still relies on electrical copper interconnects—which consume more energy and create operational costs that photonics aims to solve. By investing heavily in long-term solutions without resolving near-term bandwidth and power constraints in current-generation racks (e.g., NVL72, Blackwell), NVIDIA risks overestimating the immediacy of its infrastructure advantages while underestimating the time required for supply chain maturity in optical technologies, leaving its near-term gross margin and throughput guidance vulnerable to persistent copper-related inefficiencies.

Product and Service Breakdown of Revenue (2026)

Peer Comparison

Companies in the Semiconductors

S.No.	Ticker	Company	Market Cap	P/E	P/S	Total Debt (Qtr)
1	NVDA	Nvidia Corp	4,798.43 Bn	0.00 Bn	18.93	8.47 Bn
2	MU	Micron Technology Inc	1,164.41 Bn	0.00 Bn	12.90	5.72 Bn
3	AMD	Advanced Micro Devices Inc	882.18 Bn	0.00 Bn	23.55	3.22 Bn
4	INTC	Intel Corp	645.64 Bn	0.00 Bn	12.01	45.03 Bn
5	ALMU	Aeluma, Inc.	370.26 Bn	0.00 Bn	71,258.42	-
6	ARM	Arm Holdings Plc /Uk	358.73 Bn	427.06 Bn	72.91	-
7	TXN	Texas Instruments Inc	271.25 Bn	0.00 Bn	14.71	14.05 Bn
8	MRVL	Marvell Technology, Inc.	239.95 Bn	0.00 Bn	27.53	4.96 Bn

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Investment Thesis

Segments Breakdown of Revenue (2026)

Product and Service Breakdown of Revenue (2026)

Peer Comparison

Research Tools

Developers

About

Investment Thesis

Segments Breakdown of Revenue (2026)

Product and Service Breakdown of Revenue (2026)

Peer Comparison

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