Can an Apple M4 Pro 24GB run Qwen2.5 32B Instruct?
No
Only barely. It loads at IQ3_XXS, which compresses the weights far enough to visibly degrade the model, so the honest answer is that this pairing does not work.
The numbers
Qwen2.5 32B Instruct has 32.76 billion parameters across 64 layers. The Apple M4 Pro 24GB has 24 GB of unified memory at 273 GB/s, of which about 16.8 GB is left for a model once the operating system has taken its share.
| Quantisation | Total needed | Fits | Speed |
|---|---|---|---|
| Q8_0 | 35.03 GB | no | — |
| Q6_K | 27.63 GB | no | — |
| Q5_K_M | 24.31 GB | no | — |
| Q4_K_M | 21.03 GB | no | — |
| IQ4_XS | 18.82 GB | no | — |
| Q3_K_M | 17.53 GB | no | — |
| IQ3_XXS degraded | 14.28 GB | yes | 15.6 t/s |
| Q2_K degraded | 15.39 GB | yes | 14.4 t/s |
| IQ2_XXS degraded | 10.47 GB | yes | 21.6 t/s |
3 of the 9 levels fit. Levels marked degraded are listed for completeness, not as advice.
What to do instead
The straightforward answer is a smaller model from the same family. These do fit on a Apple M4 Pro 24GB, at a quantisation worth using:
- Qwen3 30B A3B — 30.53B at IQ4_XS, 16.28 GB, about 88.6 tokens/s
- Qwen3.5 27B — 27.78B at IQ4_XS, 16.36 GB, about 13.5 tokens/s
- Qwen3 14B — 14.77B at Q8_0, 16.48 GB, about 13.4 tokens/s
Shortening the context window will not rescue this one: at 4k it still wants 19.88 GB against 21.03 GB at 8k, because the weights rather than the cache are what fill the card. The gap here is too large for settings to close.
Or the hardware that does run it
The cheapest device we track that runs Qwen2.5 32B Instruct at a quantisation worth using is the Radeon RX 7900 XT, around $650 , running it at IQ4_XS at about 31.6 tokens per second. Check the current price (affiliate link; indicative price reviewed 2026-09-01). Compare every option.
See how fast it feels
Apple M4 Pro 24GB running Qwen2.5 32B Instruct at IQ3_XXS
YouWhy does my model use more memory when the conversation gets longer?
Because of the KV cache. Every token you send leaves behind a key and a value vector in each layer of the model, and those stay in memory for as long as the conversation lasts.
The weights are a fixed cost: load a 4-bit 8B model and that is about 4.8 GB, whether you write one word or ten thousand. The cache is the part that grows, and it grows in a straight line with the number of tokens in the window.
How steeply depends on the model's attention design. With grouped-query attention, several query heads share one key-value pair, which cuts the cache by that ratio. Without it, every head keeps its own, and a long context can cost more memory than the weights themselves.
If you are short on memory, the first thing to try is lowering the context window in your runtime, not the quantisation.
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU. The same answer on RTX 4090 at Q4_K_M would take about 4.5 s.
Keep going
Everything the Apple M4 Pro 24GB runs, the full breakdown for Qwen2.5 32B Instruct, or check a different pairing in the calculator.