Qwen 3 VL
The most powerful vision-language model in the Qwen3 family. Combines Qwen 3's reasoning with state-of-the-art visual understanding. 2B-235B sizes for any hardware.
What Qwen 3 VL is for
Qwen 3 VL takes images as well as text. Alibaba shipped it in 2025-10 with 6 sizes (2B, 4B, 8B, 30B, 32B, 235B), 128K of context and a Apache 2.0 licence. Running a vision-language model locally costs more memory than a text model of the same parameter count, because the image encoder and the projected image tokens both occupy the context. The catalogue records its strength as frontier-class vision-language with thinking.
Sizes and memory
The 2B needs about 2 GB of weights at Q4_K_M and the 235B about 130 GB, a spread of 128 GB across the family. The catalogue's default pick is 8B. Recommended VRAM adds roughly half again on top of the weights for the KV cache and the runtime.
| Size | Weights at Q4_K_M | Recommended VRAM | Smallest card in our guide that holds it |
|---|---|---|---|
| 2B | 2 GB | 3 GB | NVIDIA Jetson Orin Nano 8GB |
| 4B | 3 GB | 5 GB | NVIDIA Jetson Orin Nano 8GB |
| 8B | 6 GB | 9 GB | NVIDIA Jetson Orin Nano 8GB |
| 30B | 18 GB | 27 GB | NVIDIA RTX 3090 |
| 32B | 20 GB | 30 GB | NVIDIA RTX 3090 |
| 235B | 130 GB | 195 GB | Apple M3 Ultra |
Hardware that runs Qwen 3 VL
Every one of the 19 cards with dedicated memory in our hardware guide holds at least one size of Qwen 3 VL at Q4_K_M. The cheapest that does is the NVIDIA Jetson Orin Nano 8GB at about $199, running the 8B.
The largest size any of them holds is the 235B at 130 GB, on the Apple M3 Ultra (192 GB at 800 GB/s). Bandwidth, not capacity, sets the speed once a size fits, so a card further down the list with faster memory will generate more tokens per second on the same weights than a larger, slower one.
What people use it for
The catalogue lists 4 recorded uses for Qwen 3 VL: vision, document understanding, agentic, multimodal. It is tagged vision, multimodal, tools, thinking, long-context.
Where Qwen 3 VL sits in the Qwen family
Our catalogue holds 5 Qwen entries. Ordered by release date, Qwen 3 VL (2025-10) is the 5th of them, and the most recent.
The Apache 2.0 licence puts no usage ceiling on it, which matters if the thing you are building has users. That is the practical difference between this and a vendor community licence.
Context window: 128K
128K tokens is roughly 96,000 words at the usual ratio, which is a small repository or a whole book. Two cautions come with a window this size: the KV cache at full length can rival the weights for memory, and models rarely use the far end of their advertised context as well as they use the near end. Treat it as headroom, not as a promise.
How to run Qwen 3 VL locally
Our catalogue does not record registry tags, so look the current tag up in the Ollama library or on Hugging Face before pasting these. The commands below are the shape of the workflow, not a copy-and-paste recipe.
Option 1: Ollama (simplest)
# find the tag for Qwen 3 VL at ollama.com/library
ollama run <tag>Option 2: Mullama (production)
mullama pull <tag>
mullama run <tag>Option 3: llama.cpp (CLI)
# download a GGUF from Hugging Face, searching for "Qwen 3 VL GGUF"
./llama-cli -m model.Q4_K_M.gguf -p "Hello, AI!"Option 4: Python with Mullama or llama-cpp-python
from mullama import Model, Context
model = Model.load("model.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256))Sources
Model record from src/data/models.json (2026-06-29); card capacities from src/data/gpus.json (2026-06-29). Upstream: Curated from ollama.com/library + community benchmarks + paperswithcode + huggingface.