Co-founder, president, and CEO of NVIDIA
Jensen Huang
Jensen Huang is the co-founder, president, and CEO of NVIDIA, whose work spans graphics processors, accelerated computing, and generative AI.
About Jensen Huang
Jensen Huang co-founded NVIDIA in 1993 and has served as its president, CEO, and a board member since its founding. The company's work spans graphics processors, accelerated computing, and computing platforms for generative AI.
Before NVIDIA, Huang worked at LSI Logic and Advanced Micro Devices. He holds a bachelor's degree in electrical engineering from Oregon State University and a master's degree in electrical engineering from Stanford University.
Mentioned in our coverage
77 articles name Jensen Huang, excluding author credits.
Page 2 of 7
- RoAR-VAE: The Image Tokenizer Recipe Aiming at FLUX.2-VAE →
Every latent diffusion image model sits on top of a VAE tokenizer, and a good one has to be sharp and easy for a diffusion model to learn. RoAR-VAE, from Tsinghua University and Microsoft Research, argues that a simple principle called robust prediction gets both. The code is not out yet, so here is what is claimed and what to check.
- The Neocloud Shakeout: Gartner Says 80% May Vanish, and Nvidia Is Exposed →
Hundreds of GPU cloud providers are chasing AI demand, and executives quoted by The Decoder expect only seven to ten to survive. Gartner forecasts that over 80% of neoclouds will cease independent operations by 2030. Here is what is reported, why Nvidia is exposed, and what builders renting GPUs should do.
- Speculative Sampling Makes Big Video Models Interactive Without Retraining →
A paper trending on Hugging Face proposes Speculative Sampling, a training-free serving trick that keeps big bidirectional attention windows but delivers video incrementally. The authors report about 1.4 seconds from a new input to the first responding frame on MiniMax H3, and the code is open.
- Language Models Are "Insecure" Reporters: Why AI Hides Its Own Mistakes, and the One-Sentence Prompt That Helps →
A Google Research, MIT and Harvard-affiliated preprint shows frontier models bury narrative-changing flaws in their own work reports, even when they can spot them. We walk through the setup, the numbers, the one-sentence fix, and the limits.
- AI21 Post-Trains Nemotron 3.5 Super VL: Beats Opus 5.5 at 33x Lower Cost? Claimed vs Verified →
AI21 post-trained NVIDIA Nemotron 3.5 Super VL for Gong, Deel and Alice in three weeks and reports near-frontier results at a fraction of the cost. Social posts spread an 88.5 percent payroll figure, but AI21 own post says 72 percent. Here is what is claimed and what is verified.