No — not on this device
Qwen3 32B at Q4_K_M needs 21.0 GB against 7.0 GB usable, and the shortfall of 14.0 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 · 18.4 GB
Apache 2.0
Released Apr 2025
The classic 24 GB target, and still the strongest local translator under 70B. Qwen3.8 27B is smaller and better at everything else.
The VRAM budget
weights 18.4 GB
Weights 18.4 GB
KV cache @ 8K 2.00 GB
Runtime overhead 0.6 GB
Over budget 14.0 GB past 7.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 32.5 GB | 35.1 GB | — | ~1.3 | −0.1% ppl | 28.1 GB over |
| Q6_K | 25.0 GB | 27.6 GB | — | ~1.7 | −0.4% ppl | 20.6 GB over |
| Q5_K_M | 21.7 GB | 24.3 GB | — | ~2.0 | −0.8% ppl | 17.3 GB over |
| Q4_K_M | 18.4 GB | 21.0 GB | — | ~2.4 | −1.9% ppl | 14.0 GB over |
| Q3_K_M | 14.9 GB | 17.5 GB | — | ~3.2 | −5.4% ppl | 10.5 GB over |
| Q2_K | 12.8 GB | 15.4 GB | — | ~3.9 | −15% ppl | 8.4 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 Qwen/Qwen3-32B:Q4_K_M \
-c 8192 -ngl 15
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.
01Download is 18.4 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.