Can I run Ministral 3 8B?

Ministral 3 8B is Mistral's late-2025 take on the small, do-everything local model, and it handles both chat and vision in the same 8B package. At a 4-bit quant it comes in around 4.9 GB, so it slots onto an 8 GB GPU with room to spare and runs happily within unified memory on any Apple Silicon Mac. The 8 GB minimum RAM figure is honest for the weights alone, which makes this a realistic pick for a laptop or a cheap second-hand GPU rather than something you need a workstation to host.

In daily use it feels quick. On an RTX 3060 you can expect around 63 tokens per second at 4-bit, a 4090 pushes that to roughly 177, and an M-series Max sits near 72, all faster than you can read a streaming reply. The headline is the 256K context window, but treat it as a ceiling, not a default. At 128K of context the full memory footprint climbs to about 21.7 GB, well past what an 8 GB card holds, so keep working context to a few thousand tokens unless you have the VRAM to back the long window.

Against its siblings it splits the difference: Mistral Nemo 12B generally has more headroom for harder reasoning if you can afford the larger model, while Mistral 7B is the leaner fallback when memory is tight. Ministral 3 8B's standout trait is that vision sits in the same small footprint, so you get image understanding without jumping to a heavier multimodal model. It ships under Apache 2.0, which means you can use it commercially and in production with no provider-specific strings attached, a genuinely clean license for a model this capable.