Deploying VLMs through vLLM: The Inference Field Guide
Younes El Hjouji
The niche world of deploying LLMs for inference can be confusing. Countless precious developer hours are wasted cross-referencing between Hugging Face docs, technical reports, and vLLM or SGLang docs — all in the pursuit for that sweet spot of package versions and flag specifications that will get you to a running inference server.
At Overshoot, we know all too well that the distance between Hugging Face model weights and a deployed inference server can be minutes, but it can just as easily be days. For multi-modal LLMs, the surface area of unexpectedness is even greater.
Following our survey of all relevant open source vision language models, we want to provide the developer community with reproducible guides to deploy every vision language model. Actual snippets that are tested and reproducible. This collection of guides is something like a Bestiary for taming wild models into deployed inference endpoints. Since Bestiary is barely pronounceable, we call it the Inference Field Guide.
We will be updating both our survey and our Field Guide as we onboard and test more models. We will expand model coverage to include image models and we will enrich our guides with benchmarking results and evaluations. We welcome your requests and suggestions and hope this proves a valuable resource in the vision AI space.
To access these models even easier, try them out for free in our playground and run inference on them with just a few lines of code through our SDKs.
Inference Field Guide
Below are the deployment guides currently available. Each guide includes complete setup instructions, deployment commands, troubleshooting tips, and performance benchmarks.
Qwen3.5 Family
Qwen3-VL Family
- Qwen3-VL-2B-Instruct
- Qwen3-VL-4B-Instruct
- Qwen3-VL-8B-Instruct
- Qwen3-VL-30B-A3B-Instruct
- Qwen3-VL-32B-Instruct