Huang Loses $15 Billion as Nvidia Sinks
Nvidia's 9.3% drop, its worst since January 2025, cost Jensen Huang $15.1 billion as investors bet the midterms will slow the AI buildout.
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At a glance
NVIDIA develops accelerated computing hardware and software. Coverage follows its GPUs, CUDA ecosystem, AI infrastructure and business decisions.
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- GPUs; CUDA; AI systems
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Reviewed 19 October 2026. Dates retain the precision of the source; retrospective disclosures are identified in the event. Fiscal-year, quarterly and Data Center sales are labelled separately; none is presented as ARR.
Company milestones
Revenue, funding, product launches and major company events, newest first. Expand the earlier milestones for more history.
Q2 FY2027 revenue reaches $96.2B
Quarterly revenue grows 106% year over year; Data Center contributes $89.0B, up 117%. These are quarterly sales, not ARR.
Source: NVIDIAVera Rubin ramps into full production
NVIDIA reports partner deployments of its new platform, extending the hardware roadmap beyond Blackwell.
Source: NVIDIA
Show earlier milestonesHide earlier milestones2026–1993 · 10 milestones
Earlier revenue, financing and company milestones.
FY2026 revenue rises to $215.9B
Full-year revenue increases 65%; Data Center revenue reaches $193.7B. Fiscal 2026 ends in January 2026.
Source: NVIDIAAnnounces a 10GW OpenAI infrastructure plan
The companies sign a letter of intent. Planned capacity and contemplated investment are not recorded as delivered capacity or revenue.
Source: OpenAI / NVIDIAFY2025 revenue reaches $130.5B
Annual sales grow 114%; fourth-quarter Data Center revenue is $35.6B, showing the scale of the generative-AI infrastructure expansion.
Source: NVIDIAIntroduces Blackwell
The new GPU platform integrates compute, networking and software for large-scale model training and inference.
Source: NVIDIACompletes the $7B Mellanox acquisition
High-performance networking becomes a larger part of NVIDIA’s data-center platform alongside GPUs.
Source: NVIDIAIntroduces the DGX-1 AI system
The integrated GPU server combines accelerators, interconnects and optimized software for deep-learning development.
Source: NVIDIAAlexNet demonstrates GPU-trained deep learning
The ImageNet breakthrough uses NVIDIA GPUs, marking an important step in the shift from graphics hardware to AI computing.
Source: NVIDIA retrospectiveIntroduces CUDA
The programming platform opens GPU acceleration to general-purpose computing, becoming a foundation for later deep-learning workloads.
Source: NVIDIAGoes public
NVIDIA’s investor FAQ dates its public-market debut to January 22, before its later expansion into general-purpose GPU computing.
Source: NVIDIANVIDIA is founded
The company starts in computer graphics before expanding into accelerated computing and AI infrastructure.
Source: NVIDIA
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