Can I run Qwen 2.5 VL 7B?

Qwen 2.5 VL 7B is Alibaba's vision-language model, which is the part that matters: alongside chat it reads images, screenshots, charts, and documents, so it is the one to reach for when your prompt includes a picture and not just text. At 8.3B parameters it stays compact. A 4-bit quant lands around 5 GB, which fits on an 8 GB GPU and sits easily within unified memory on any Apple Silicon Mac. The minimum 8 GB RAM figure is realistic for the weights, though vision work and longer context push you higher in practice.

In daily use it feels quick for its class. On an RTX 3060 12GB you can expect roughly 61 tokens per second at 4-bit, and an RTX 4090 pushes that to around 170, fast enough that replies stream past reading speed. An M-series Max sits near 69 tok/s, while CPU-only on DDR5 drags down to about 10. The 128K context is genuine, but it is a ceiling, not a cruising altitude: filling it drives total memory to roughly 22 GB, well past an 8 GB card, so keep working context modest unless you have the headroom.

For plain text chat, a dedicated text model like Granite 3.3 8B at a similar size tends to be a tidier pick, since it is built for that and skips the vision overhead. Qwen 2.5 VL 7B's standout trait is exactly that overhead paying off: native image understanding in a model small enough to self-host, which the tiny Qwen 3 0.6B and 1.7B cannot touch. It ships under Apache 2.0, so you can use it commercially and in production without license worry, which is rare for capable multimodal weights.