Magistral Small 2507 needs ~21.4 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
9.5 tok/s
TTFT
20344 ms
Safe context
27K
Memory
21.4 GB / 23.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Tight fit | 9.5 tok/s | 11097 ms | 27K |
| Coding | S | Tight fit | 9.5 tok/s | 20344 ms | 27K |
| Agentic Coding | S | Runs with offload | 5.5 tok/s | 51232 ms | 27K |
| Reasoning | S | Tight fit | 9.5 tok/s | 24043 ms | 27K |
| RAG | S | Runs with offload (needs ~0.5 GB host RAM) | 8.9 tok/s | 39714 ms | 27K |
How Magistral Small 2507 (24B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | S91 |
Q3_K_S | 3 | 11.8 GB | Low | S92 |
NVFP4 | 4 |
Copy-paste commands to run Magistral Small 2507 on your machine.
Run
ollama run magistralYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 11.7 tok/s | |
| Qwen 3.5 27B | 27B | S | 8.6 tok/s |
Yes, Mac mini M4 32GB can run Magistral Small 2507 with a S grade (Tight fit). Expected decode speed: 9.5 tok/s.
Magistral Small 2507 (24B parameters) requires approximately 21.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Magistral Small 2507 is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 32GB, Magistral Small 2507 achieves approximately 9.5 tokens per second decode speed with a time-to-first-token of 20344ms using Q4_K_M quantization.
For coding workloads, Magistral Small 2507 on Mac mini M4 32GB receives a S grade with 9.5 tok/s and 27K context.
On Mac mini M4 32GB, Magistral Small 2507 can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/magistral-small-2507-on-m4-mini-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
13.4 GB |
| Medium |
| S92 |
Q4_K_M | 4 | 14.6 GB | Medium | S91 |
Q5_K_MBest for your GPU | 5 | 17.3 GB | High | S91 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
| Qwen 3.6 27B | 27B | S | 7.1 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 12.4 tok/s |
| Qwen 3.5 35B A3B | 35B | A | 10.2 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. Mac mini M4 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.