No — not on this device
Llama 3.3 70B Instruct at Q4_K_M needs 42.8 GB against 25.6 GB usable, and the shortfall of 17.2 GB is more than 16 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
Llama 3.3 Community
Released Dec 2024
Still the creative-writing favourite: consistent voice, takes direction. Needs 48 GB to sit comfortably on GPU at Q4.
What hardware do I need for Llama 3.3 70B Instruct? →
Fits instead: Llama 3.1 8B Instruct (6.1 GB) · Llama 3.2 3B Instruct (3.3 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 meta-llama/Llama-3.3-70B-Instruct: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.