NVILA: Efficient Frontier Visual Language Models (2412.04468v2)

Abstract: Visual LLMs (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a family of open VLMs designed to optimize both efficiency and accuracy. Building on top of VILA, we improve its model architecture by first scaling up the spatial and temporal resolutions, and then compressing visual tokens. This "scale-then-compress" approach enables NVILA to efficiently process high-resolution images and long videos. We also conduct a systematic investigation to enhance the efficiency of NVILA throughout its entire lifecycle, from training and fine-tuning to deployment. NVILA matches or surpasses the accuracy of many leading open and proprietary VLMs across a wide range of image and video benchmarks. At the same time, it reduces training costs by 4.5X, fine-tuning memory usage by 3.4X, pre-filling latency by 1.6-2.2X, and decoding latency by 1.2-2.8X. We will soon make our code and models available to facilitate reproducibility.

• The paper introduces a scale-then-compress framework that balances high-resolution detail extraction with computational efficiency.
• It achieves significant resource savings with training cost reduced by 4.5× and fine-tuning memory by 3.4×, all while preserving benchmark accuracy.
• NVILA's innovations enable robust VLM performance in resource-constrained scenarios, supporting applications like edge computing and autonomous systems.

Overview of "NVILA: Efficient Frontier Visual LLMs"

In the field of visual LLMs (VLMs), achieving a balance between efficiency and accuracy remains a pivotal challenge. The paper, "NVILA: Efficient Frontier Visual LLMs," introduces NVILA, which provides a systematic approach to optimizing both these aspects. Leveraging the foundational work established by VILA, this paper presents architectural improvements and methodological strategies that significantly enhance the operational efficiency of visual LLMs without compromising on performance.

Key Contributions

NVILA's primary contribution is the introduction of a "scale-then-compress" methodological framework that facilitates efficient processing of high-resolution images and long videos. This approach strategically scales up spatial and temporal resolutions to capture more detail from visual inputs, thus elevating the accuracy ceiling. Subsequently, visual tokens are compressed, thus optimizing computational resources.

Key numerical results illustrate that NVILA achieves notable efficiency improvements: training costs are reduced by 4.5×, fine-tuning memory usage drops by 3.4×, pre-filling latency decreases by 1.6–2.2×, and decoding latency by 1.2–2.8×. Despite these resource savings, NVILA maintains or exceeds the accuracy of leading open and proprietary VLMs across numerous benchmarks.

Methodological Advances

1. Scale-Then-Compress Approach: By increasing image and video resolution, NVILA captures more detailed visual information. Subsequently, the compression of visual tokens enhances processing efficiency. This dual-phase strategy allows NVILA models to maintain high accuracy with fewer computational demands.
2. Lifecycle Efficiency: Through comprehensive optimization across training, fine-tuning, and deployment phases, NVILA achieves a considerable reduction in resource requirements. Notable innovations include leveraging FP8 precision for training and compression algorithms for both spatial and temporal tokens.
3. Efficient Data Utilization and Pruning: Utilizing DeltaLoss, NVILA prunes datasets intelligently, effectively maintaining model performance while reducing data redundancy. This selection criterion ensures that only the most informative data samples influence model training.
4. Fine-Tuning and Deployment Optimization: The paper explores novel parameter-efficient fine-tuning strategies that cater to the unique architecture of NVILA. Additionally, W8A8 quantization and FP16 accumulation optimization contribute to enhanced deployment performance.

Implications and Future Directions

NVILA sets a precedent in the design of VLMs that prioritize efficiency without yielding to the pressure of dropping accuracy. This equilibrium opens avenues for broader applications, notably in areas with constrained computational resources, such as edge devices, robotics, and autonomous systems. Furthermore, the integration of temporal localization capabilities and medical domain applications illustrates the versatility and scope of NVILA.

The theoretical implications are significant as well. The methods demonstrated in NVILA could inspire further research into efficient architecture designs not only within the field of VLMs but across other AI domains where large-scale data processing is involved.

In anticipation of future developments, the NVILA framework suggests pathways for further research into efficient model scaling and adaptation for different application domains. As NVILA's code and models become available, it is poised to serve as a valuable baseline for subsequent work in visual language modeling, fostering innovation and practical impacts across various technological landscapes.