Can I run DeepSeek R1 32B?

DeepSeek R1 32B is a reasoning model for people who want chain-of-thought work done locally rather than quick chat. At 32.8B parameters it is not a casual download: a 4-bit quant is about 19.9 GB, and you want at least 32 GB of RAM to run it comfortably. It will not fit on a 12 GB card like an RTX 3060, so realistically you are looking at a 24 GB GPU, a high-memory Apple Silicon Mac, or a workstation with plenty of system memory. If you have that hardware, it is one of the better open-weight reasoners you can self-host.

In daily use it thinks out loud before answering, which is the point but also the cost: every reply spends tokens reasoning, so raw speed matters. On an RTX 4090 you can expect roughly 43 tokens per second at 4-bit, fast enough that the visible reasoning stream stays readable; on an Apple M Max it drops to around 18 tok/s, and on a CPU with DDR5 it crawls at about 3 tok/s, which is workable for batch jobs but not interactive. The 128K context is generous, but filling it is expensive here: at the full window total memory climbs to about 51.6 GB, so plan for a fat machine or keep context modest.

Against Qwen 3 32B, which is the same size and also handles reasoning, DeepSeek R1 32B tends to lean harder into explicit step-by-step deliberation while Qwen 3 32B generally feels more balanced for mixed chat-plus-reasoning work. R1 32B's standout trait is that visible reasoning trace: for math, logic, and multi-step problems where you want to see and check the model's working, it is hard to beat at this size. It ships under the MIT license, so you can use it commercially and in production without the provider-specific terms that come with some open-weight families.