Supporting 1024 Frames with Nearly 100% Accuracy: NVIDIA's 'LongVILA' Powers Up for Long Videos

Discover NVIDIA's LongVILA: A full-stack solution for training and deploying long-context visual language models (VLMs) with enhanced performance and scalability.

LongVILA is a new full-stack solution for long-context visual language models (VLMs), combining system design, model training, and dataset development.

Integrating multimodal understanding with long-context capabilities is crucial. Models supporting multiple modalities can handle more flexible inputs, allowing diverse interactions. The ability to process longer contexts enables models to handle more information, such as long documents and videos, which is essential for real-world applications.

Currently, some work on long-context VLMs uses simplified methods rather than comprehensive solutions. However, a full-stack approach is vital for long-context VLMs.

Training large models is complex and requires coordinated design between data engineering and system software. Unlike text-only LLMs, VLMs (e.g., LLaVA) need unique architectures and flexible distributed training strategies. Additionally, long-context modeling demands both long-context data and infrastructure that supports memory-intensive training.