Magistral Small 2507 needs ~20.0 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~35 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 with offload
Decode
35.2 tok/s
TTFT
5493 ms
Safe context
16K
Memory
20.0 GB / 20.0 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.
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 | 35.2 tok/s | 2996 ms | 16K |
| Coding | S | Runs with offload | 35.2 tok/s | 5493 ms | 16K |
| Agentic Coding | A | Very compromised (needs ~1.6 GB host RAM) | 20.8 tok/s | 13552 ms | 16K |
| Reasoning | S | Runs with offload | 35.2 tok/s | 6492 ms | 16K |
| RAG | A | Very compromised (needs ~1.6 GB host RAM) | 20.8 tok/s | 16940 ms |
How Magistral Small 2507 (24B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | S92 |
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 | 40.7 tok/s | |
| Qwen 3.5 27B | 27B | A | 18.3 tok/s |
Yes, RX 7900 XT 20GB can run Magistral Small 2507 with a S grade (Runs with offload). Expected decode speed: 35.2 tok/s.
Magistral Small 2507 (24B parameters) requires approximately 20.0 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 RX 7900 XT 20GB, Magistral Small 2507 achieves approximately 35.2 tokens per second decode speed with a time-to-first-token of 5493ms using Q4_K_M quantization.
For coding workloads, Magistral Small 2507 on RX 7900 XT 20GB receives a S grade with 35.2 tok/s and 16K context.
On RX 7900 XT 20GB, Magistral Small 2507 can safely use up to 16K 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-rx-7900-xt-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 16K |
13.4 GB |
| Medium |
| S92 |
Q4_K_MBest for your GPU | 4 | 14.6 GB | Medium | S91 |
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 |
| Qwen 3.6 27B | 27B | S | 17.3 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | A | 43.3 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.