Magistral Small 2507 needs ~24.9 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~23 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
Runs well
Decode
23.2 tok/s
TTFT
8362 ms
Safe context
131K
Memory
24.9 GB / 46.1 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 23.2 tok/s | 4561 ms | 131K |
| Coding | S | Runs well | 23.2 tok/s | 8362 ms | 131K |
| Agentic Coding | S | Runs well | 23.2 tok/s | 12162 ms | 131K |
| Reasoning | S | Runs well | 23.2 tok/s | 9882 ms | 131K |
| RAG | S | Runs well | 23.2 tok/s | 15203 ms | 131K |
How Magistral Small 2507 (24B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | A85 |
Q3_K_S | 3 | 11.8 GB | Low | S85 |
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 | S | 31.8 tok/s | |
| Qwen 3.5 27B | 27B | S | 22.7 tok/s |
Yes, MacBook Pro M4 Pro 64GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 23.2 tok/s.
Magistral Small 2507 (24B parameters) requires approximately 24.9 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 MacBook Pro M4 Pro 64GB, Magistral Small 2507 achieves approximately 23.2 tokens per second decode speed with a time-to-first-token of 8362ms using Q4_K_M quantization.
For coding workloads, Magistral Small 2507 on MacBook Pro M4 Pro 64GB receives a S grade with 23.2 tok/s and 131K context.
On MacBook Pro M4 Pro 64GB, Magistral Small 2507 can safely use up to 131K 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-pro-64gb" 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 |
| S86 |
Q4_K_M | 4 | 14.6 GB | Medium | S86 |
Q5_K_M | 5 | 17.3 GB | High | S87 |
Q6_K | 6 | 19.7 GB | High | S88 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | S90 |
F16 | 16 | 49.2 GB | Maximum | F0 |
| Qwen 3.6 27B | 27B | S | 17.3 tok/s |
| Qwen 3.6 35B A3B | 35B | S | 29.4 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 32.9 tok/s |
Not always. MacBook Pro M4 Pro 64GB 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.