HyperAI

1. Tutorial Introduction

FastVLM is an efficient visual language model (VLM) launched by the Apple team in September 2025, which can improve the efficiency and performance of high-resolution image processing. The model introduces the new hybrid visual encoder FastViTHD, which effectively reduces the number of visual tokens and significantly reduces the encoding time. While maintaining similar performance to existing VLMs, FastVLM greatly improves the processing speed. For example, in the LLaVA-1.5 setting, the time to first token generation (TTFT) is shortened by 3.2 times compared to other models. FastVLM performs well in a variety of VLM benchmarks, and the model size is smaller and the training data requirements are less, demonstrating high efficiency and practicality in multimodal understanding tasks. The relevant paper results are "FastVLM: Efficient Vision Encoding for Vision Language Models", which 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

🖌️ If you see a high-quality project, please leave a message in the background to recommend it! In addition, we have also established a tutorial exchange group. Welcome friends to scan the QR code and remark [SD Tutorial] to join the group to discuss various technical issues and share application effects↓

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},
}