Llama 3.1 70B Instruct
Llama 3.1 70B Instruct loads in 42 GB at Q4_K_M, comfortably from 64 GB of memory, Macs included, starting at the MacBook Pro M5 Max 128GB. Below is the memory math we ran for it, the cheapest card that fits, and the fastest machine we track.
How much memory does Llama 3.1 70B Instruct need?
Llama 3.1 70B Instruct is a 70B dense model built for quality, coding. The weights alone take 42 GB at Q4_K_M; add context and the OS, and it belongs in the 64 GB memory class. Every number below comes from the ModelFit engine: speeds are estimates, labeled est., because we do not benchmark hardware ourselves.
KV = fp16 estimate (q8_0 cache roughly halves it). "Comfortable" = weights + KV within the engine's tiered budget (~70-85% of memory).
Which machines run Llama 3.1 70B Instruct?
Verdicts come from the same engine that powers our fit checker: memory capacity first, bandwidth for speed. How we compute these numbers · Hugging Face model card
More Llama models
Get told when a better model fits your MacBook Air M5 16 GB
Get one email when a new open-weight model is a better fit for this machine.
For: MacBook Air M5 16 GB
Frequently asked questions
How much memory does Llama 3.1 70B Instruct need?
42 GB for the Q4_K_M weights, plus ~5.0 GB of KV-cache at 16k context, about 47.0 GB total. Comfortable from 64 GB of VRAM or unified memory.
Does Llama 3.1 70B Instruct run on a Mac?
Yes, from the MacBook Pro M5 Max 128GB (~10 tok/s est.). Unified memory means the RAM budget is the only limit.
What is the cheapest GPU for Llama 3.1 70B Instruct?
The NVIDIA RTX PRO 6000 Blackwell (96 GB VRAM) is the cheapest tracked card that runs Llama 3.1 70B Instruct comfortably, ~23 tok/s est.
Cite this page
ModelFit: Llama 3.1 70B Instruct, specs, memory math and hardware verdicts. https://modelfit.io/models/llama3.1-70b/ (dataset updated 2026-10-03, CC BY 4.0).