NVIDIA

Reporting and context on NVIDIA, with a closer look at the decisions and developments that matter.

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NVIDIA
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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.

Focus
GPUs; CUDA; AI systems
Reference
NVIDIA

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.

  1. 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: NVIDIA
  2. Vera 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.

  1. FY2026 revenue rises to $215.9B

    Full-year revenue increases 65%; Data Center revenue reaches $193.7B. Fiscal 2026 ends in January 2026.

    Source: NVIDIA
  2. Announces 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 / NVIDIA
  3. FY2025 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: NVIDIA
  4. Introduces Blackwell

    The new GPU platform integrates compute, networking and software for large-scale model training and inference.

    Source: NVIDIA
  5. Completes the $7B Mellanox acquisition

    High-performance networking becomes a larger part of NVIDIA’s data-center platform alongside GPUs.

    Source: NVIDIA
  6. Introduces the DGX-1 AI system

    The integrated GPU server combines accelerators, interconnects and optimized software for deep-learning development.

    Source: NVIDIA
  7. AlexNet 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 retrospective
  8. Introduces CUDA

    The programming platform opens GPU acceleration to general-purpose computing, becoming a foundation for later deep-learning workloads.

    Source: NVIDIA
  9. Goes public

    NVIDIA’s investor FAQ dates its public-market debut to January 22, before its later expansion into general-purpose GPU computing.

    Source: NVIDIA
  10. NVIDIA is founded

    The company starts in computer graphics before expanding into accelerated computing and AI infrastructure.

    Source: NVIDIA

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