NVIDIA

NVIDIA says the 64GB DGX Spark configuration will become available from Acer, ASUS, Dell, Gigabyte, HP and MSI on October 23, 2026, starting at $4,999.

NVIDIA market capitalisation as of 1 October 2026, up 23% year-to-date, making NVIDIA the most valuable public company in history at that point. Growth driven by continued AI infrastructure spending and record quarterly revenues exceeding $96 billion.

NVIDIA launched an open full-stack agent safety platform combining OpenShell secure runtime and the Sentry reference design.

NVIDIA agreed to acquire Hugging Face while committing to preserve its open, multi-cloud and multi-accelerator model platform.

NVIDIA reported Q2 fiscal 2027 revenue of $96.2 billion, up 106% year-over-year and 18% sequentially. Data Center revenue was $89.0 billion (+117% YoY). Net income was $59.7 billion (GAAP). Jensen Huang stated 'AI has reached its inflection point' and demand is accelerating. Q3 FY2027 guidance set at $108 billion.

NVIDIA announced an entry-level robotics computer with higher local model inference performance and lower power use.

NVIDIA put its Groq 3 LPX interactive inference accelerator into full production for ultrafast token generation.

NVIDIA released Alpamayo 2 Super under an open commercial license for autonomous-vehicle development.

NVIDIA introduced an edge-optimized Cosmos 3 model for real-time physical AI systems.

NVIDIA made the 550-billion-parameter Nemotron 3 Ultra open model available for long-running agent workflows.

NVIDIA announced the Vera Rubin platform had ramped into full production for agentic AI factories.

NVIDIA announced an open humanoid reference design to accelerate physical AI research and development.

NVIDIA launched Cosmos 3 as an open frontier world foundation model for physical AI and formed the Cosmos Coalition.

NVIDIA introduced a 34-billion-parameter open reasoning vision-language-action model for level 4 robotaxi development.

NVIDIA reported Q1 fiscal 2027 record revenue of $81.6 billion, up 85% year-over-year and 20% sequentially. Data Center revenue reached a record $75.2 billion (+92% YoY). Net income was $58.3 billion (GAAP). NVIDIA approved an additional $80 billion share repurchase and raised the quarterly dividend from $0.01 to $0.25 per share.

NVIDIA released an open multimodal model unifying vision, audio and language for efficient agents.

NVIDIA formed a coalition of AI labs to develop an open frontier model that will underpin the upcoming Nemotron 4 family.

NVIDIA introduced Nemotron 3 Omni and VoiceChat, GR00T N1.7, Alpamayo 1.5 and Proteina-Complexa.

NVIDIA announced NemoClaw, combining Nemotron models and OpenShell in a governed stack for OpenClaw agents.

NVIDIA launched open software, models, blueprints and agent skills for enterprise agent development.

NVIDIA released its Vera Rubin DSX AI factory reference design and made the Omniverse DSX Blueprint generally available.

NVIDIA announced seven Vera Rubin platform chips in full production for agentic AI factories.

NVIDIA released the open 120-billion-parameter Nemotron 3 Super model for complex agentic AI systems.

NVIDIA reported Q4 fiscal 2026 record revenue of $68.1 billion, up 73% year-over-year and 20% sequentially. Data Center revenue was $62.3 billion. Full-year FY2026 revenue reached $215.9 billion (+65% YoY), with full-year net income of $120.1 billion (GAAP).

NVIDIA released Cosmos Transfer 2.5, Predict 2.5 and Reason 2, updated GR00T N1.6, and introduced Jetson T4000.

NVIDIA market capitalisation at end of calendar year 2025, up 38% year-over-year. NVIDIA remained the world's most valuable public company, sustained by record data centre AI GPU demand.

NVIDIA introduced Nemotron 3 Nano, Super and Ultra open models and companion reinforcement-learning and evaluation tools.

NVIDIA reported Q3 fiscal 2026 record revenue of $57.0 billion, up 62% year-over-year and 22% sequentially. Data Center revenue was $51.2 billion (+66% YoY). Net income was $31.9 billion (GAAP). Jensen Huang noted 'Blackwell sales are off the charts, and cloud GPUs are sold out.'

NVIDIA began shipping DGX Spark personal AI supercomputers to developers.

NVIDIA released GR00T N1.6, Cosmos Reason NIM and new Newton physics integration for Isaac Lab.

OpenAI and NVIDIA signed a letter of intent to deploy at least 10 gigawatts of NVIDIA systems, with the first Vera Rubin gigawatt targeted for the second half of 2026.

NVIDIA unveiled Rubin CPX, a GPU class designed for massive-context inference, with availability expected by the end of 2026.

NVIDIA reported Q2 fiscal 2026 revenue of $46.7 billion, up 56% year-over-year and 6% sequentially. Data Center revenue reached $41.1 billion. Net income was $26.4 billion (GAAP). NVIDIA approved an additional $60 billion share repurchase authorization.

NVIDIA began availability of its Blackwell-powered Jetson Thor robotics computer.

NVIDIA expanded its robotics stack with updated GR00T and Cosmos models and new Omniverse libraries.

NVIDIA expanded DGX Cloud Lepton with European cloud providers, AWS and Azure participation, and Hugging Face training integration.

NVIDIA reported Q1 fiscal 2026 revenue of $44.1 billion, up 69% year-over-year and 12% sequentially. Data Center revenue was $39.1 billion (+73% YoY). Net income was $18.8 billion (GAAP). Results included a $4.5 billion charge related to H20 export control restrictions.

NVIDIA announced an AI compute marketplace connecting developers to GPU capacity across global cloud partners.

NVIDIA introduced a reference design for AI-enabled enterprise storage and query-agent infrastructure.

NVIDIA introduced personal AI computers for local model prototyping and development.

NVIDIA expanded Cosmos with Reason, Transfer and Predict models plus physical AI data tools.

NVIDIA released GR00T N1, an open foundation model for humanoid robot reasoning and skills.

NVIDIA launched a blueprint for enterprise research agents that reason over business data.

NVIDIA released Nano, Super and Ultra open reasoning models for building agentic AI systems.

NVIDIA launched Dynamo to orchestrate and scale distributed generative AI inference.

NVIDIA introduced a Blackwell Ultra DGX SuperPOD design and Mission Control operations software for AI factories.

NVIDIA announced B300 and GB300 Blackwell Ultra systems optimized for AI reasoning.

NVIDIA launched Cosmos with open world foundation models, tokenizers, guardrails and data-processing tools for physical AI.

NVIDIA market capitalisation at end of calendar year 2024, up 169% year-over-year, briefly surpassing Apple and Microsoft to become the world's most valuable public company.

NVIDIA and partners launched reusable reference workflows for enterprise generative AI agents.

NVIDIA launched AI Foundry with NeMo, DGX Cloud and NIM services for building and deploying customized Llama 3.1 models.

NVIDIA made ACE generative AI microservices generally available for cloud-based digital-human applications.

NVIDIA entered a definitive agreement to acquire AI workload orchestration provider Run:ai.

NVIDIA and AWS announced broader Blackwell infrastructure and generative AI software integrations.

NVIDIA and Microsoft expanded integrations spanning Azure AI, NIM, Omniverse and enterprise generative AI.

NVIDIA introduced the Blackwell GPU architecture, B100, B200 and GB200 Grace Blackwell Superchip for generative AI.

NVIDIA launched NIM, containerized optimized inference microservices for generative AI deployment.

NVIDIA announced DGX GB200 and a Blackwell-powered DGX SuperPOD reference architecture.

NVIDIA launched Earth-2 cloud APIs and AI models for high-resolution weather and climate simulation.

NVIDIA announced a general-purpose foundation model initiative for humanoid robots.

NVIDIA introduced production generative AI microservices for interactive digital humans.

NVIDIA market capitalisation at end of calendar year 2023, up 240% year-over-year, driven by explosive demand for AI training GPUs following the ChatGPT-led generative AI boom.

NVIDIA introduced H200, adding HBM3e memory to the Hopper platform for generative AI and HPC.

NVIDIA publicly released its open-source TensorRT-LLM library for optimized large-model inference.

NVIDIA expanded Omniverse with generative AI tools and deeper OpenUSD workflows for industrial digitalization.

NVIDIA introduced the L40S data-center GPU for generative AI, inference, training and graphics workloads.

NVIDIA introduced AI Workbench for creating and moving generative AI projects across local, data-center and cloud systems.

NVIDIA announced DGX GH200, connecting 256 Grace Hopper Superchips into a large shared-memory AI system.

NVIDIA launched DGX Cloud, providing browser-based access to managed AI supercomputing infrastructure.

NVIDIA announced broad H100 availability through leading cloud and server providers as generative AI demand surged.

NVIDIA introduced cloud services for enterprises to build custom language and visual generative AI models.

NVIDIA market capitalisation at end of calendar year 2022, down 51% from the prior year amid a broader semiconductor and tech sector downturn.

NVIDIA introduced BioNeMo, a generative AI service for chemistry and biomolecular research.

NVIDIA expanded TAO Toolkit integration for fine-tuning and optimizing pretrained production AI models.

NVIDIA made its end-to-end GPU-accelerated recommender AI framework generally available.

NVIDIA made its multilingual speech AI SDK generally available.

NVIDIA announced its fourth-generation DGX system powered by eight H100 GPUs.

NVIDIA launched Jetson AGX Orin for next-generation robotics and autonomous machines.

NVIDIA announced the Grace Hopper Superchip, pairing a Grace CPU with a Hopper GPU using coherent memory.

NVIDIA announced the Hopper architecture and H100 GPU with a Transformer Engine for large AI models.

NVIDIA made its AI Enterprise software suite generally available.

NVIDIA made Fleet Command generally available for centrally deploying and managing edge AI applications.

NVIDIA launched a hosted enterprise program offering immediate access to its end-to-end AI infrastructure and software stack.

NVIDIA introduced Morpheus for real-time AI-assisted threat detection using GPUs and BlueField DPUs.

NVIDIA introduced Base Command to provision, schedule and operate shared DGX SuperPOD AI infrastructure.

NVIDIA announced an enterprise software suite for virtualized AI workloads on VMware vSphere.

NVIDIA opened the Omniverse collaboration and simulation platform to beta users.

NVIDIA introduced a deskside AI system with four A100 GPUs for workgroups.

NVIDIA doubled A100 accelerator memory to 80GB using HBM2e.

NVIDIA introduced Maxine, a cloud-native suite of GPU-accelerated AI video-conferencing and streaming software.

NVIDIA began shipping DGX A100, an integrated 5-petaflop AI system with eight A100 GPUs.

NVIDIA launched the A100 Tensor Core GPU, its first Ampere data-center accelerator for training and inference.

NVIDIA expanded EGX with certified servers, software and ecosystem support for AI, IoT and 5G at the edge.

NVIDIA introduced EGX to bring real-time AI computing to edge and industrial environments.

NVIDIA launched RAPIDS as an open-source GPU-accelerated data science and machine-learning suite.

NVIDIA launched the T4 GPU and TensorRT 5 as a data-center inference platform.

NVIDIA introduced the Jetson AGX Xavier embedded AI computer for autonomous machines.

NVIDIA introduced Clara as a medical imaging and healthcare AI computing platform.

NVIDIA introduced DGX-2 with 16 V100 GPUs connected by NVSwitch for large-scale AI training.

NVIDIA launched TITAN V, bringing Volta Tensor Core AI acceleration to desktop systems.

NVIDIA introduced its GPU Cloud catalog, later branded NGC, for optimized deep-learning containers and software.

NVIDIA introduced the Volta architecture and V100 GPU, including Tensor Cores built for deep learning.

The Pascal-based Tesla P100 accelerator debuted alongside DGX-1 for deep learning and HPC.

NVIDIA unveiled DGX-1, its first integrated deep-learning supercomputer.

NVIDIA released the first version of its GPU-accelerated deep neural network primitives library.

AlexNet, trained on NVIDIA GPUs, won the ImageNet competition and helped catalyze modern deep learning adoption.

NVIDIA introduced CUDA and a C programming environment, opening its GPUs to general-purpose parallel computing.