Magistral Small 2507 needs ~31.8 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~34 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
34.1 tok/s
TTFT
5682 ms
Safe context
131K
Memory
31.8 GB / 92.2 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 | 34.1 tok/s | 3099 ms | 131K |
| Coding | S | Runs well | 34.1 tok/s | 5682 ms | 131K |
| Agentic Coding | S | Runs well | 34.1 tok/s | 8265 ms | 131K |
| Reasoning | S | Runs well | 34.1 tok/s | 6715 ms | 131K |
| RAG | S | Runs well | 34.1 tok/s | 10331 ms | 131K |
How Magistral Small 2507 (24B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | A81 |
Q3_K_S | 3 | 11.8 GB | Low | A81 |
NVFP4 | 4 |
Copy-paste commands to run Magistral Small 2507 on your machine.
Run
ollama run magistralYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 6.3 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, Mac Studio M2 Ultra 128GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 34.1 tok/s.
Magistral Small 2507 (24B parameters) requires approximately 31.8 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 Studio M2 Ultra 128GB, Magistral Small 2507 achieves approximately 34.1 tokens per second decode speed with a time-to-first-token of 5682ms using Q4_K_M quantization.
For coding workloads, Magistral Small 2507 on Mac Studio M2 Ultra 128GB receives a S grade with 34.1 tok/s and 131K context.
On Mac Studio M2 Ultra 128GB, 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-m2-ultra-128gb" 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 |
| A81 |
Q4_K_M | 4 | 14.6 GB | Medium | A82 |
Q5_K_M | 5 | 17.3 GB | High | A82 |
Q6_K | 6 | 19.7 GB | High | A82 |
Q8_0 | 8 | 25.7 GB | Very High | A83 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | S88 |
| 70.2 tok/s |
| Qwen 3.5 27B | 27B | S | 30.4 tok/s |
| Qwen 3.6 27B | 27B | S | 23.1 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 28.9 tok/s |
Not always. Mac Studio M2 Ultra 128GB 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.