Vision
How to deploy Qwen3-VL-32B-Instruct on a GPU cloud
A 33.4B vision-language model that reads images alongside text. Full specs, license and use cases.
Qwen3-VL-32B-Instruct size and hardware requirements
33.4B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
74.6 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 62.1 GB | 74.6 GB | A100 | 1 | $0.851/hr |
| FP8 (quantized) | 31.1 GB | 37.3 GB | RTX 6000 Ada | 1 | $0.524/hr |
| INT4 (quantized) | 15.5 GB | 18.6 GB | RTX 3090 | 1 | $0.147/hr |
How to run Qwen3-VL-32B-Instruct
Run Qwen3-VL-32B-Instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve Qwen/Qwen3-VL-32B-Instruct --tensor-parallel-size 1Run Qwen3-VL-32B-Instruct with Ollama
Verified against Ollama's own library listing.
ollama run qwen3-vl:32bRun Qwen3-VL-32B-Instruct with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Qwen3-VL-32B-Instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Qwen3-VL-32B-Instruct-GGUFSource: https://huggingface.co/unsloth/Qwen3-VL-32B-Instruct-GGUF
Deploy Qwen3-VL-32B-Instruct on Aquanode
Aquanode has no one-click deploy template for Qwen3-VL-32B-Instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× A100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod