All events
Most recently updated firstOct19
Nvidia Data Center GPUs: Hopper, Blackwell and Vera Rubin
Nvidia announced the Hopper H100 in March 2022 and said it began shipping the GPU in November that year; Hopper was later extended with the H200. Nvidia introduced Blackwell in March 2024, but reports of a delay were followed by Nvidia’s disclosure that it had changed the GPU mask to improve yields before its fourth-quarter production ramp.
Oct14
GeForce and DLSS: From the RTX 50 Launch to 3D-Guided Neural Rendering
Nvidia introduced the Blackwell-based GeForce RTX 50 series at CES 2025, from the $299 RTX 5060 to the $1,999 RTX 5090, while DLSS 4 made Multi Frame Generation exclusive to the new generation. Through 2026, Nvidia expanded DLSS 4.5 with updated Super Resolution, Dynamic Multi Frame Generation and Ray Reconstruction, then launched DLSS 5’s 3D-Guided Neural Rendering in NBA 2K27 for RTX 50 desktop and laptop GPUs.
Oct14
Nvidia's Dealmaking: From Mellanox and the Failed Arm Bid to Groq and Hugging Face
Nvidia began this period with the $7 billion Mellanox acquisition, which added networking to its data-center business, then failed to buy Arm after opposition from competition authorities in the US, UK and EU. It has since bought AI-infrastructure software and data companies, while also using licence-plus-hire structures for Enfabrica and Groq rather than outright acquisitions.
Oct14
US Export Controls on Nvidia AI Chips to China: A100 to H20 to H200
Washington first required licenses for Nvidia’s A100 and H100 exports to China in August 2022, then broadened the restrictions in 2022 and 2023, closing the thresholds that had allowed Nvidia’s China-specific A800 and H800. Nvidia responded with the H20, but the United States subjected that chip to licensing in April 2025, prompting a US$4.5 billion charge and lost sales; later licenses did not restore meaningful H20 sales.
Oct09
How Nvidia Came to Own AI Compute: From CUDA to the H100 Shortage
Nvidia’s AI-computing position began with CUDA in 2006, which made GPUs programmable for general-purpose parallel computing. AlexNet’s 2012 ImageNet result showed that a deep neural network could be trained effectively with GPUs, and Nvidia later built Tensor Cores and data-center products such as the V100 and A100 around AI workloads.
