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Open models that love hardware
Models
A 9B decision model picks every move of Dino, Tetris and Pinball, on the device, in real time.
Pick a game
Every move is the model's own choice. Play shows one of its recorded games at random.
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Click an object in your video and watch it tracked frame by frame on a Jetson.
A clip of up to 60 s and 8 MiB (MP4/H.264 or WebM). Every frame is tracked (up to 30 a second) for a clip of up to 15 s; a longer one at a lower rate, at least 10 frames a second, up to 450 frames.
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A full 3D body mesh from a single photo.
A photo with one or more people (up to 5, the largest first). The detector finds them; each is cropped by their box padded 1.25× and made square, warped to 512×512 and normalized with ImageNet's mean and std (upstream's top-down transform).
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Turn a few words into a picture, right on the device.
Try an example prompt
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Take a photo, or pick one, and ask about it.
An image for the prompt to ask about (optional), sent as an OpenAI image_url in the same user message; encoded by the model's visual engine.
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Reasoning about the physical world from what a camera sees.
An image for the prompt to ask about, sent as an OpenAI image_url in the same user message; encoded by the model's visual engine.
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Real-time object detection, every object boxed and named.
A photo: JPEG, PNG or WebP, up to 20 MiB. serve.py turns it upright (its EXIF orientation), resizes it to a square 560×560 (bilinear, antialiased; no letterbox) and normalizes it with ImageNet's mean and std into the engine's input [1, 3, 560, 560].
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Show it a scene and ask what happens next.
An image for the prompt to ask about (optional), sent as an OpenAI image_url in the same user message; encoded by the model's visual engine.
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Name any object in words and it finds it in the photo.
A photo; boxes use its upright pixels.
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How far away is everything? A depth map from a single photo.
One photo, ≤8 MiB / 16 MP; EXIF upright, fixed bicubic 518×518 square, ImageNet normalization.
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Click an object in your video and watch it tracked on an entry-level Jetson.
A clip of up to 60 s and 8 MiB (MP4/H.264 or WebM). Every frame is tracked (up to 30 a second) for a clip of up to 6 s; a longer one at a lower rate, at least 10 frames a second, up to 180 frames.
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Instruction-following robot policy for manipulation tasks.
Try an example instruction
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See what a vision backbone sees. Every patch of your photo coloured by what it is.
One photo, ≤8 MiB / 16 MP; EXIF upright, shorter side resized to 256, centre 224×224 crop, ImageNet normalization.
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