Trained a simple world model for my robot arm.... It predicts the future over 20000 times faster than real time on a single NVIDIA RTX 3090 GPU (128 batch -> 160x faster each).show more

We're releasing OVIE, a novel view generation model trained... entirely on single images. No multi-view datasets needed. Given a single image, it generates novel views of any scene in real time, running orders of magnitude faster than competing approaches.show more

The leading model runnable on a single cloud H100... GPU now fits on a single home GPU! 🔥 We've optimized Gemma 3 27B with QAT so you can run our best-in-class open model on your desktop RTX 3090 or similar. See how easy it is to try via ollama! 👇show more

MotionStream Real-Time Video Generation with Interactive Motion Controls model... runs in real time on a single NVIDIA H100 GPU (29 FPS, 0.4s Latency)show more

A humanoid robot won a half-marathon race for robots... and humans in Beijing on Sunday. The winning robot ran several minutes faster than the men's world record.show more

Early results from my AI training runs. I've trained... my $200 robot arm on a simple picking task using imitation learning. It has learned to control the robot arm using only camera images and joint states.show more

EAGLE-3 introduces two key innovations: Training-Time Testing (TTT) and... multi-level feature fusion. By removing the feature prediction constraint used in previous EAGLE versions and leveraging semantic features across multiple layers, EAGLE-3 achieves higher acceptance rates, faster generation, and lossless performance. The result? - 5.6× faster than vanilla decoding (13B) Compared to EAGLE-1, EAGLE-3 delivers a 1.8× speedup on the 13B model, with the future EAGLE-4 release expected to further improve decoding efficiency. *Inference on the video conducted on 2x RTX 3090 GPUs at fp16 precision using the Vicuna 13B model.show more

Pothole detection on the road in real time using... Ultralytics YOLO26! 🕳️ Manual road inspections are slow, costly, and hard to scale. With object detection, potholes can be identified directly from street-level images or video feeds, enabling faster and more consistent road condition monitoring. How I built this demo: ✅ Trained a segmentation model on a custom dataset. ✅ Generated mask contours for each pothole. ✅ Leveraged the onnx-exported model for faster processing. #Pothole #RoadDamage #AIshow more

💡HunyuanVideo1.5 Update: We are now releasing the 480p I2V... step-distilled model, which generates videos in 8 or 12 steps (recommended)! On RTX 4090, end-to-end generation time is reduced by 75%, and a single RTX 4090 can generate videos within 75 seconds. The step-distilled model maintains comparable quality to the original model while achieving significant speedup. For even faster generation, you can also try 4 steps (faster speed with slightly reduced quality). 🔗Check out the GitHub Repo:show more

Diffusion for virtual worlds: we've trained a new model... to create 3D objects from text. And it's 50x faster than any alternative. We made each of these cute characters in just one minute with Genmo's new text-to-3D model. Build your own world atshow more

Too bad this humanoid will lose its job to... a faster, lower cost robot arm.show more

NVIDIA just made AI detect objects 10x faster by... deleting one step. It's called LocateAnything, and it removes the biggest bottleneck no one else was fixing in vision-language models. Normally a model builds each bounding box one coordinate token at a time. 100 objects means thousands of tokens before an answer. NVIDIA scrapped that: their Parallel Box Decoding predicts the whole box in a single forward pass, as one atomic unit. → 12.7 boxes/sec on one H100 → 10x faster than Qwen3-VL → +3.8% F1 on LVIS, accuracy up, not down → 3B params, runs on one consumer GPU Treating the box as one unit keeps its coordinates tied together, which is why accuracy climbed instead of falling. One model handles detection, GUI grounding, OCR, and document understanding, ready for computer-use agents, robotics, and document pipelines. 100% open source, weights, code, demo, and paper all live.show more

it's open source time, with a real leap for... world models 🎉 NVIDIA's SANA-WM: a camera-conditioned world model that fits on one GPU. 60s of 720p in 34s on a single 5090 - 2.6B params and Apache 2.0!show more

nvidia/LocateAnything-3B: 12.7 boxes per second on one H100 (10x... faster than Qwen3-VL) is a no joke.show more

this is the worst local AI will ever be.... tomorrow it gets faster. next month the models get smarter. next year your GPU runs what a data center runs today. Qwen3.5-35B-A3B on a single 3090. told it to visualize its own expert routing. 256 experts, 8 active per token, rendered in 3D on the same GPU running inference. no API key. no subscription. no permission needed. closed AI isn't losing ground. it's losing the argument.show more

For over two decades we've helped evolve the game.... 🎮🕹️ From DLSS to RTX Remix, #AIonRTX is pushing your gaming further & faster. Comment #AIonRTX with your favorite RTX game and why to win an ASUS Australia & New Zealand Dual GeForce RTX 4070 SUPER GPU! Learn more:show more

NVIDIA just dropped Nemotron-3-Nano:4b — a tiny 2.8GB model.... Guess whose hardware runs it the fastest? - RTX 4090: 226 tok/s - RTX 3090: 187 tok/s - Mac Studio M2 Ultra: 86 tok/s - Mac Mini M4: 25 tok/s Home court advantage is real. Also trying a new layout with live performance charts. Lmk what you think!show more

Chatterbox Turbo by Resemble AI now on MLX 🚀🎉... You can now run it locally on your Mac and it supports voice cloning and emotion control. I'm getting 3.8x faster than real-time. > pip install -U mlx-audio Model collection 👇🏽show more

Real-time MLB home run alerts. Faster than your feed.