No — not on this device
DeepSeek-R1-Distill-Llama 70B at Q4_K_M needs 42.8 GB against 25.6 GB usable, and the shortfall of 17.2 GB is more than 32 GB of system RAM can cover at a tolerable speed. A smaller sibling or a lower quantisation is the honest answer here.
Does not fit
8K context
Q4_K_M · 39.7 GB
MIT / Llama 3.3 Community
Released Jan 2025
The strongest of the R1 distills, and the one that most needs 48 GB or more.
What hardware do I need for DeepSeek-R1-Distill-Llama 70B? →
Fits instead: DeepSeek-R1-Distill-Qwen 32B (21.0 GB) · DeepSeek-R1-Distill-Qwen 14B (10.4 GB)
The VRAM budget
weights 39.7 GB
Weights 39.7 GB
KV cache @ 8K 2.50 GB
Runtime overhead 0.6 GB
Over budget 17.2 GB past 25.6 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 69.9 GB | 73.0 GB | — | ~0.3 | −0.1% ppl | 47.4 GB over |
| Q6_K | 53.9 GB | 57.0 GB | — | ~0.4 | −0.4% ppl | 31.4 GB over |
| Q5_K_M | 46.6 GB | 49.7 GB | — | ~0.5 | −0.8% ppl | 24.1 GB over |
| Q4_K_M | 39.7 GB | 42.8 GB | — | ~0.5 | −1.9% ppl | 17.2 GB over |
| Q3_K_M | 32.1 GB | 35.2 GB | — | ~0.7 | −5.4% ppl | 9.6 GB over |
| Q2_K | 27.5 GB | 30.6 GB | — | ~0.8 | −15% ppl | 5.0 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.
How to run it
$ llama-server \
-hf deepseek-ai/DeepSeek-R1-Distill-Llama-70B:Q4_K_M \
-c 8192 -ngl 45
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.
01Download is 39.7 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.
02With no GPU, thread count matters more than clock. Start at one thread per physical core.
03If it falls back to CPU silently, drop the context first, then step down a quantisation.