Can I run Qwen 3 14B?

Qwen 3 14B is the model you reach for when an 8B feels a little thin but you do not want to step up to a 30B-class download. At 14.8B parameters it is a dense model, so the whole thing is active on every token, and at a 4-bit quant it lands around 9 GB. That is a touch too large for a 12 GB card to hold comfortably with much context, but it sits well on a 16 GB GPU and runs nicely within unified memory on a higher-RAM Apple Silicon Mac. The minimum you want is about 16 GB of RAM. It is built for chat and reasoning rather than vision or heavy code generation.

In daily use it feels noticeably more deliberate than a small model, especially on multi-step prompts where its reasoning mode earns its keep. On an RTX 3060 12GB you can expect around 34 tokens per second at 4-bit, and an M-series Max sits close behind at roughly 39; a 4090 pushes it to about 95, which is well past reading speed. The 128K context window is real but expensive: fill it and total memory climbs to around 31 GB, so on anything short of a 24 GB-plus setup you will want to keep working context to a few thousand tokens.

Against DeepSeek R1 14B, which shares the same parameter count, Qwen 3 14B is the more general-purpose pick: R1 tends to lean harder into long chain-of-thought reasoning, while Qwen 3 14B generally feels more balanced for everyday chat and structured instruction-following. If you want something far lighter from the same family, Qwen 3 1.7B drops the footprint dramatically at the cost of depth. Qwen 3 14B's standout trait is that it ships under Apache 2.0, so you can use it commercially and in production with no provider-specific strings attached, which is rare at this capability level.