No — not on this device
Qwen3 235B-A22B at Q4_K_M needs 134.2 GB against 25.6 GB usable, and the shortfall of 108.6 GB is more than 64 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 108.6 GB past 25.6 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 232.5 GB | 234.6 GB | — | ~0.4 | −0.1% ppl | 209.0 GB over |
| Q6_K | 179.5 GB | 181.5 GB | — | ~0.6 | −0.4% ppl | 155.9 GB over |
| Q5_K_M | 155.1 GB | 157.2 GB | — | ~0.7 | −0.8% ppl | 131.6 GB over |
| Q4_K_M | 132.1 GB | 134.2 GB | — | ~0.8 | −1.9% ppl | 108.6 GB over |
| Q3_K_M | 107.0 GB | 109.0 GB | — | ~0.9 | −5.4% ppl | 83.4 GB over |
| Q2_K | 91.6 GB | 93.7 GB | — | ~1.1 | −15% ppl | 68.1 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 16
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.
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.