No — not on this device
Qwen3.6 27B at Q4_K_M needs 16.7 GB against 10.7 GB usable, and the shortfall of 6.0 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 · 15.6 GB
Apache 2.0
Released 22 Apr 2026
Vision
The 24 GB coding pick of spring 2026 (77.2 SWE-bench Verified). Same shape as 3.8, one generation behind.
The VRAM budget
weights 15.6 GB
Weights 15.6 GB
KV cache @ 8K 0.50 GB
Runtime overhead 0.6 GB
Over budget 6.0 GB past 10.7 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 27.5 GB | 28.6 GB | — | ~1.4 | −0.1% ppl | 17.9 GB over |
| Q6_K | 21.2 GB | 22.3 GB | — | ~1.8 | −0.4% ppl | 11.6 GB over |
| Q5_K_M | 18.4 GB | 19.5 GB | — | ~2.1 | −0.8% ppl | 8.8 GB over |
| Q4_K_M | 15.6 GB | 16.7 GB | — | ~2.4 | −1.9% ppl | 6.0 GB over |
| Q3_K_M | 12.7 GB | 13.8 GB | — | ~3.0 | −5.4% ppl | 3.1 GB over |
| Q2_K | 10.8 GB | 11.9 GB | — | ~3.5 | −15% ppl | 1.2 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. Only 16 of its 64 blocks keep a per-token KV cache; the rest are linear-attention, Mamba or convolution blocks with a fixed-size state.
How to run it
$ pip install mlx-lm $ mlx_lm.generate --model mlx-community/Qwen3.6-27B-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 15.6 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.
02macOS caps what the GPU may wire down at about 10.7 GB of 16 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.
03If it falls back to CPU silently, drop the context first, then step down a quantisation.