DeepSeek-R1 Distill Llama 70B
DeepSeek-R1 Distill Llama 70B 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 DeepSeek-R1 Distill Llama 70B need?
DeepSeek-R1 Distill Llama 70B is a 70B dense model built for reasoning, quality. 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 DeepSeek-R1 Distill Llama 70B?
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
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Frequently asked questions
How much memory does DeepSeek-R1 Distill Llama 70B 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 DeepSeek-R1 Distill Llama 70B 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 DeepSeek-R1 Distill Llama 70B?
The NVIDIA RTX PRO 6000 Blackwell is the cheapest tracked card that runs DeepSeek-R1 Distill Llama 70B comfortably — ~23 tok/s est. at ~$12,912 used (as of 2026-07-31).
Cite this page
ModelFit: DeepSeek-R1 Distill Llama 70B — specs, memory math and hardware verdicts. https://modelfit.io/models/deepseek-r1-distill-llama-70b/ (dataset updated 2026-09-03, CC BY 4.0).