No — not on this device
Llama 3.3 70B Instruct at Q4_K_M needs 42.8 GB against 14.4 GB usable, and the shortfall of 28.4 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
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 28.4 GB past 14.4 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 69.9 GB | 73.0 GB | — | ~0.6 | −0.1% ppl | 58.6 GB over |
| Q6_K | 53.9 GB | 57.0 GB | — | ~0.8 | −0.4% ppl | 42.6 GB over |
| Q5_K_M | 46.6 GB | 49.7 GB | — | ~1.0 | −0.8% ppl | 35.3 GB over |
| Q4_K_M | 39.7 GB | 42.8 GB | — | ~1.2 | −1.9% ppl | 28.4 GB over |
| Q3_K_M | 32.1 GB | 35.2 GB | — | ~1.7 | −5.4% ppl | 20.8 GB over |
| Q2_K | 27.5 GB | 30.6 GB | — | ~2.1 | −15% ppl | 16.2 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 22
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.
02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.
03If it falls back to CPU silently, drop the context first, then step down a quantisation.