Can I run Llama 3.1 70B?

Llama 3.1 70B is the model you reach for when 8B-class assistants keep coming up short and you have real hardware to throw at it. At 4-bit it weighs about 42.8 GB, and the fact that matters most is what it does not fit on: a 12 GB RTX 3060 and even a 24 GB RTX 4090 are both out, since the weights alone overflow them. Realistically this is a 64 GB-minimum machine, and a Mac Studio with a lot of unified memory is the most painless single-box home for it. Drop to a 2-bit quant near 29.6 GB if you are squeezing, but you pay for it in quality.

In daily use it feels deliberate rather than snappy. On an Apple M-Max you are looking at roughly 8 tokens per second at 4-bit, which is readable but slower than you talk, and on a DDR5 CPU it crawls at around 1 token per second, fine for batch jobs and not much else. The 128K context window is genuine, but it is expensive here: filling it pushes total memory to about 87.5 GB, well past the 64 GB floor. Keep working context modest unless you have headroom to spare, or the machine starts swapping and the already-modest speed falls apart.

Against its own family this is the heavyweight: Llama 3.2 3B and 1B are the ones you actually run on a laptop, while 70B is the one you run when answer quality outranks speed. If your real goal is step-by-step reasoning, a same-sized DeepSeek R1 70B generally pulls ahead on that specific axis, where this Llama is tuned for chat. Its standout trait is broad, mature tooling and dependable general-purpose output. One caveat: the Llama Community license is open-weight, not open-source, so check Meta's terms before shipping it in a commercial product.