Magistral Small 2507 needs ~23.1 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~83 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
82.9 tok/s
TTFT
2335 ms
Safe context
131K
Memory
23.1 GB / 48.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 82.9 tok/s | 1274 ms | 131K |
| Coding | S | Runs well | 82.9 tok/s | 2335 ms | 131K |
| Agentic Coding | S | Runs well | 82.9 tok/s | 3397 ms | 131K |
| Reasoning | S | Runs well | 82.9 tok/s | 2760 ms | 131K |
| RAG | S | Runs well | 82.9 tok/s | 4246 ms | 131K |
How Magistral Small 2507 (24B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | A84 |
Q3_K_S | 3 | 11.8 GB | Low | A85 |
NVFP4 | 4 | 13.4 GB | Medium | S85 |
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 | S87 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | S90 |
F16 | 16 | 49.2 GB | Maximum | F0 |
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 | 170.7 tok/s | |
| Qwen 3.5 27B | 27B | S | 74 tok/s | |
| Qwen 3.6 27B | 27B | S | 74.3 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 143.5 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 176.6 tok/s |
Yes, RTX PRO 5000 Blackwell 48GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 82.9 tok/s.
Magistral Small 2507 (24B parameters) requires approximately 23.1 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 RTX PRO 5000 Blackwell 48GB, Magistral Small 2507 achieves approximately 82.9 tokens per second decode speed with a time-to-first-token of 2335ms using Q4_K_M quantization.
For coding workloads, Magistral Small 2507 on RTX PRO 5000 Blackwell 48GB receives a S grade with 82.9 tok/s and 131K context.
On RTX PRO 5000 Blackwell 48GB, 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-rtx-pro-5000-blackwell-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: