1. Tutorial Introduction

FastVLM, released by Apple in September 2025, is a high-performance visual language model (VLM) that improves the efficiency and performance of high-resolution image processing. The model introduces the novel FastViTHD hybrid visual encoder, effectively reducing the number of visual tokens and significantly lowering encoding time. While maintaining similar performance to existing VLMs, FastVLM significantly improves processing speed; for example, in the LLaVA-1.5 setting, it reduces the time to first token generation (TTFT) by 3.2 times compared to other models. FastVLM performs excellently on various VLM benchmarks and has a smaller model size and requires less training data, demonstrating its efficiency and practicality in multimodal understanding tasks. Related research papers are available. FastVLM: Efficient Vision Encoding for Vision Language ModelsIt has been included in CVPR 2025.

The project provides two models of models:

• FastVLM-0.5B
• FastVLM-7B

This tutorial uses resources for a single RTX 4090 card.

2. Project Examples

3. Operation steps

1. After starting the container, click the API address to enter the Web interface

2. Usage steps

If "Bad Gateway" is displayed, it means the model is initializing. Since the model is large, please wait about 2-3 minutes and refresh the page.

4. Discussion

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Citation Information

The citation information for this project is as follows:

@InProceedings{fastvlm2025,
  author = {Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokul Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, Hadi Pouransari},
  title = {FastVLM: Efficient Vision Encoding for Vision Language Models},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2025},
}