No — not on this device
Qwen3 235B-A22B at Q4_K_M needs 134.2 GB against 46.4 GB usable, and the shortfall of 87.8 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 · 132.1 GB
Apache 2.0
Released Apr 2025
Workstation class. Realistically a 192 GB unified-memory or multi-GPU model.
What hardware do I need for Qwen3 235B-A22B? →
Fits instead: Qwen3 32B (21.0 GB) · Qwen3 Coder 30B-A3B (18.5 GB)
The VRAM budget
weights 132.1 GB
Weights 132.1 GB
KV cache @ 8K 1.47 GB
Runtime overhead 0.6 GB
Over budget 87.8 GB past 46.4 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 232.5 GB | 234.6 GB | — | ~0.8 | −0.1% ppl | 188.2 GB over |
| Q6_K | 179.5 GB | 181.5 GB | — | ~1.1 | −0.4% ppl | 135.1 GB over |
| Q5_K_M | 155.1 GB | 157.2 GB | — | ~1.3 | −0.8% ppl | 110.8 GB over |
| Q4_K_M | 132.1 GB | 134.2 GB | — | ~1.6 | −1.9% ppl | 87.8 GB over |
| Q3_K_M | 107.0 GB | 109.0 GB | — | ~2.3 | −5.4% ppl | 62.6 GB over |
| Q2_K | 91.6 GB | 93.7 GB | — | ~3.0 | −15% ppl | 47.3 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/Qwen3-235B-A22B:Q4_K_M \
-c 8192 -ngl 31
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.
01Download is 132.1 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.