Magistral Small 2507 needs ~19.7 GB but MacBook Pro M2 Pro 16GB only has 11.5 GB. Try a smaller quantization or lighter model.
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
8.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.2 tok/s
TTFT
37314 ms
Safe context
4K
Memory
19.7 GB / 11.5 GB
Offload
40%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 19.7 GB, but this setup only exposes 11.5 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.6 tok/s | 18957 ms | 4K |
| Coding | F | Too heavy | 5.2 tok/s | 37314 ms | 4K |
| Agentic Coding | F | Too heavy | 4.6 tok/s | 60873 ms | 4K |
| Reasoning | F | Too heavy | 5.2 tok/s | 44098 ms | 4K |
| RAG | F | Too heavy | 4.6 tok/s | 76091 ms | 4K |
How Magistral Small 2507 (24B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | F0 |
Q3_K_S | 3 | 11.8 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$799 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
RTX 5090 32GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
No, Magistral Small 2507 requires more memory than MacBook Pro M2 Pro 16GB provides.
Magistral Small 2507 (24B parameters) requires approximately 19.7 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 M2 Pro 16GB, Magistral Small 2507 achieves approximately 5.2 tokens per second decode speed with a time-to-first-token of 37314ms using Q4_K_M quantization.
For coding workloads, Magistral Small 2507 on MacBook Pro M2 Pro 16GB receives a F grade with 5.2 tok/s and 4K context.
On MacBook Pro M2 Pro 16GB, Magistral Small 2507 can safely use up to 4K 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-m2-pro-16gb" 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 |
| F0 |
Q4_K_M | 4 | 14.6 GB | Medium | F0 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Pro M2 Pro 16GB 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.
