Getting Started with Qwen3.5 Vision-Language Models

Vision Language models are one of the most powerful and highest potential applications of deep learning technologies. The reasoning behind such a strong assertion lies in the versatility of VL modeling: from document understanding to object tracking to image captioning, vision language models are likely going to be the building blocks of the incipient, physical AI future. This is because everything that we can interact with that will be powered by AI - from robots to driverless vehicles to medical assistants - will likely have a VL model in its pipeline.

This is why the power of open-source development is so important to all of these disciplines and applications of AI, and why we are so excited about the release of Qwen3.5 from Qwen Team. This suite of completely open source VL models, ranging in size from .8B to 397B (with activated 17B) parameters, is the clear next step forward for VL modeling. They excel at bench marks for everything from agentic coding to computer use to document understanding, and nearly match closed source rivals in terms of capabilities.

In this tutorial, we will examine and show how to make the best use of Qwen3.5 using a DigitalOcean GPU Droplet. Follow along for explicit instructions on how to setup and run your GPU Droplet to power Qwen3.5 to power applications like Claude Code and Codex using your own resources.