No — not on this device
Qwen2.5-Coder 32B at Q4_K_M needs 21.0 GB against 18.0 GB usable, and the shortfall of 3.0 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 · 18.4 GB
Apache 2.0
Released Nov 2024
The first local code model that felt competitive with hosted assistants. Qwen3.8 27B is smaller and far ahead on agentic work.
What hardware do I need for Qwen2.5-Coder 32B? →
Fits instead: Qwen2.5-Coder 14B (10.4 GB) · Qwen2.5-Coder 7B (5.3 GB)
The VRAM budget
weights 18.4 GB
Weights 18.4 GB
KV cache @ 8K 2.00 GB
Runtime overhead 0.6 GB
Over budget 3.0 GB past 18.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 32.5 GB | 35.1 GB | — | ~1.7 | −0.1% ppl | 17.1 GB over |
| Q6_K | 25.0 GB | 27.6 GB | — | ~2.2 | −0.4% ppl | 9.6 GB over |
| Q5_K_M | 21.7 GB | 24.3 GB | — | ~2.6 | −0.8% ppl | 6.3 GB over |
| Q4_K_M | 18.4 GB | 21.0 GB | — | ~3.0 | −1.9% ppl | 3.0 GB over |
| Q3_K_M | 14.9 GB | 17.5 GB | 9K | 3.7 | −5.4% ppl | Fits |
| Q2_K | 12.8 GB | 15.4 GB | 18K | 4.4 | −15% ppl | Long context |
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
$ pip install mlx-lm $ mlx_lm.generate --model mlx-community/Qwen2.5-Coder-32B-Instruct-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 18.4 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 18.0 GB of 24 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.