VILA is NVIDIA’s family of efficient vision-language models for understanding images, multiple images, and video. It is designed to balance accuracy with efficiency, so capable multimodal models can run from the data center down to edge devices like Jetson boards.
The VILA family has grown through several generations: VILA-1.5 added video understanding in sizes from 3B to 40B parameters, NVILA (VILA 2.0) reworked the full stack for cheaper training and faster deployment, LongVILA pushes context beyond one million tokens for long videos, and VILA-HD pairs a high-resolution vision encoder for detail-rich images. AWQ-quantized 4-bit versions deploy efficiently through TinyChat and TensorRT-LLM on hardware ranging from A100 servers to laptops and Jetson Orin devices. The project publishes training code, evaluation code, datasets, and checkpoints, and the family now also feeds into NVIDIA’s Cosmos Nemotron vision-language line. The code is Apache-2.0 licensed, the model weights use CC BY-NC 4.0, and the repo has over 3,800 stars with updates continuing into 2026.
VILA is free to use: the code is Apache-2.0 licensed and the model weights are released under CC BY-NC 4.0 for research and non-commercial use. There is no paid tier.
Multimodal AI researchers and developers building video understanding, visual question answering, or edge-deployed vision-language applications.