NVIDIA AI

@MichaelGannotti @Shaughnessy119 ⏳️

@Shaughnessy119 I am really hoping we see some killer open source LLMs come out from @NVIDIAAI AI soon that are frontier level challenging models

As Chinese AI models go closed source, and all U.S. frontier models are closed, there is a massive opportunity for a western open source AI Lab A future lack of competitive open source AI models is a net negative for humanity, low cost intelligence and sovereignty

The latent-vs-pixel debate misses the point. GPT Image 2 shows what users notice: pixel-level fidelity. Latent models show what scales: compact semantic structure. We connect them by replacing VAE/RAE decoders with a Pixel Diffusion Decoder. Code and Model available: research.nvidia.com/labs/sil/proje… 🧵(1/N)

From the Lab: Text Diffusion and Elastic Reasoning | Nemotron Labs x.com/i/broadcasts/1…

@tmophoto 🔥

A month ago i discovered the true power of the DGX spark by @NVIDIAAI Its nice to see everyone is getting on the concurrency train now. This is old but it still applies to almost every model i have tested on the spark. when you get above 8-16 requests things really start slowing down from prompt processing but these are lightning fast around 8 concurrent requests.

@aijoey @nvidia @NVIDIAAIDev Well done @aijoey! 🙌

15 concurrent terminal workloads on a local DGX Spark, all served by nvidia/nemotron-3-super 120B A12B NVFP4 through vLLM. 15/15 completed, 0 errors, 30.2s wall time: no fake dashboard, just local inference under concurrent load. fineprint: live local concurrency demo

Good explainer on world models - well done @juliarturc

@IntelFactorAI @flir 🙌

@NVIDIAAI @flir wiko.com.hk 😁

3 million units of kitchen knives, fast lead times, low defect rates? This is how factory AI actually works. @NVIDIAAI + @flir spotting defects on knife blades in real time with our API - you can have a fleet.

@AlicanKiraz0 🔥

@NVIDIAAI 🦾🔥

(2x DGX Sparks) + MiniMax M2.7 NVFP4 = 16 local AI agents running simultaneously 👀

@cindy_x_wu 🙌

(Mini) career update: Just wrapped up an amazing year interning at NVIDIA Spatial Intelligence Lab, it's been a wonderful journey exploring the frontier of video generation and world models with so many brilliant people. Super grateful for the amazing mentors, colleagues, and friends who supported, inspired and believed in me throughout this chapter!

@EtienneTRGC Nice! 🙌

Welcome buddy... DGX

@dgarieck 🙌

@MattNiessner Face2Face was a legendary paper! You forgot to include that it got you in the NVIDIA booth at GTC 😉

Always a pleasure to be back at Stanford! 🌲 It's been almost a decade since my four years here as a Visiting Professor. I was fortunate to work with so many world-class researchers who helped shape my career. Many of those same colleagues are now leading frontier labs all over the world. Research-wise, it was a wild time. Deep learning was just starting to take over traditional computer vision, and generative methods were barely working (remember the early VAEs and GANs?). We were one of the groups pushing early 3D deep learning. It was exciting, but chaotic. There was no PyTorch, and even simple operators had to be hand-written in CUDA. Lots of fun! Our main focus was 3D scene reconstruction and semantic scene understanding. For scanning, we used Microsoft Kinects with methods like Voxel Hashing or BundleFusion, which led to scene understanding works like ScanNet and Matterport3D. And, of course, 3D face reconstruction—which led to the legendary Face2Face paper by @JustusThies, got me on Jimmy Kimmel Live, and ultimately sparked the foundation of @synthesiaIO. Coming back brings up so much nostalgia for those days before massive transformers took over. But beyond the research, a lot has evolved. On campus, new dorms have transformed the landscape, there's a shiny new data science building, and an incredible lineup of new CS faculty. Palo Alto has changed, too. There’s a new bikeway on El Camino, and Cal Ave is now a pedestrian zone with many shops having changed. (Un)surprisingly, the Nuthouse didn't survive. The building sits empty like one of its many peanut shells. Still, it's a vibrant area with fantastic food, great to hang out after an intense day of research. Die Luft der Freiheit weht!

@DataChaz Thanks for sharing Charly - super cool work 🙌

NVIDIA JUST DROPPED AN OPEN-SOURCE WORLD MODEL THAT RUNS ON YOUR GAMING PC 🤯 Instead of melting your GPU, @NVIDIAAI’s new 2.6B-parameter model, SANA-WM, brings 60 seconds of controllable video straight to your laptop. > Just feed it an image > a prompt > a camera path .. and you can literally fly through the scene 🔥 It works on less than 8GB of VRAM and plugs right into tools you already use like ComfyUI and Diffusers. The core advantages: → full 6-axis camera control → 36x faster than older open models → uses extreme 32x compression to stay tiny → upscale to 2K using the LTX2 refiner Best part? It's 100% free and open-source (Apache-2.0 licensed) repo link in 🧵↓

@AiXsatoshi 💚

1枚600WのGPUを3枚装備した

@firstadopter 💚

Imagine graduating college with a comp sci degree, announcing on LinkedIn that you got a job at Nvidia and having Dwight Diercks!?! leave a congratulations comment on your post.

@GregEstes @twominutepapers @liu_mingyu 👀

@twominutepapers @liu_mingyu Two of my favorite people. Lots of brains and general awesomeness in one photo.

Spent a bit of time learning from my friend and legendary researcher @liu_mingyu. Huge honor, thank you! Btw, he is hiring - more info below.

@LyalinDotCom @NVIDIAAIDev 🙌

Setting up a DGX Spark! Unboxing time. Setup is stupid easy. Plugin. Connect to its WiFi hotspot and use web UI to configure account, connect it to your home network and it updates the device for you. ❤️🔥

@TheAhmadOsman ⏳️

@NVIDIAAI When 👀👀👀

Models that I can not wait to run on my AI cluster at home in 2026 > MiniMax M3 (Multimodal) > NVIDIA Nemotron 3 Ultra (~500B) > Kimi K3 > DeepSeek V4 This is going to be the year of Local LLMs