Overview
VILA is an NVlabs project providing a family of efficient vision-language models that improve inference speed and understanding capability across edge, datacenter and cloud deployments. The repository includes training code, evaluation scripts and multiple pretrained checkpoints, supporting multi-image and long-video scenarios.
Key features
- Optimized for video and multi-image tasks with low-latency deployment and high throughput.
- Supports AWQ quantization and lightweight deployment backends like TinyChat and TinyChatEngine.
- Provides full training, evaluation and inference toolchains, and releases multiple checkpoints for reproducibility.
Use cases
- Video understanding and captioning.
- Multi-image reasoning and image question answering.
- Efficient inference on edge and embedded devices.
Technical details
- Implemented in PyTorch with efficiency-oriented model designs and quantization support (AWQ).
- Includes tools for long-video and multimodal evaluation (LongVILA, vila-eval).
- Offers documentation, demos and Hugging Face model collections for integration and replication.