Can I run DeepSeek R1 70B?

DeepSeek R1 70B is the model you reach for when you want chain-of-thought reasoning running entirely on your own hardware, not a quick chat helper. At 4-bit it weighs about 42.8 GB and needs around 64 GB of RAM minimum, which rules out a single consumer GPU outright: it will not fit on an RTX 3060 12GB or even an RTX 4090 24GB. The realistic home for it is a Mac with plenty of unified memory or a workstation with 64 GB or more, where the full set of weights can actually stay resident. This is a serious commitment, not a casual download.

In daily use the honest headline is that it's slow. On an Apple M-Max you're looking at roughly 8 tokens per second, and on a CPU with DDR5 it drops to about 1 token per second, so replies arrive at reading pace at best and crawl at worst. Because R1 thinks out loud before answering, a lot of those tokens go into visible reasoning, so a single response can take a while. The 128K context is there, but filling it pushes total memory to about 87.5 GB, well past the 64 GB floor, so keep working context modest unless you have headroom to spare.

Against its own family the trade is straightforward: DeepSeek R1 7B or the tiny 1.5B run far faster and fit on modest hardware, but this 70B generally holds up better on harder multi-step reasoning where the smaller distills tend to lose the thread. Compared to Llama 3.1 70B, which is tuned for chat, R1 70B's standout trait is that explicit reasoning style, better suited to math and logic problems than open conversation. The MIT license is the easy part here: you can use it commercially and in production without provider-specific strings attached. Just budget for the memory and the wait.