Flash-VL 2B: Optimizing Vision-Language Model Performance for Ultra-Low Latency and High Throughput
Journal:
arXiv
Published Date:
May 14, 2025
Abstract
In this paper, we introduce Flash-VL 2B, a novel approach to optimizing
Vision-Language Models (VLMs) for real-time applications, targeting ultra-low
latency and high throughput without sacrificing accuracy. Leveraging advanced
architectural enhancements and efficient computational strategies, Flash-VL 2B
is designed to maximize throughput by reducing processing time while
maintaining competitive performance across multiple vision-language benchmarks.
Our approach includes tailored architectural choices, token compression
mechanisms, data curation, training schemes, and a novel image processing
technique called implicit semantic stitching that effectively balances
computational load and model performance. Through extensive evaluations on 11
standard VLM benchmarks, we demonstrate that Flash-VL 2B achieves
state-of-the-art results in both speed and accuracy, making it a promising
solution for deployment in resource-constrained environments and large-scale
real-time applications.
