Magistral Small 2507 needs ~19.1 GB but RTX 5060 Ti 8GB only has 8.0 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
11.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.1 tok/s
TTFT
63282 ms
Safe context
4K
Memory
19.1 GB / 8.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 19.1 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.1 tok/s | 34517 ms | 4K |
| Coding | F | Too heavy | 3.1 tok/s | 63282 ms | 4K |
| Agentic Coding | F | Too heavy | 3.1 tok/s | 92046 ms | 4K |
| Reasoning | F | Too heavy | 3.1 tok/s | 74787 ms | 4K |
| RAG | F | Too heavy | 3.1 tok/s | 115057 ms | 4K |
How Magistral Small 2507 (24B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 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
RTX 4000 Ada 20GBBudget pickMakes 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.
~$1,250 MSRP
RTX 3090 24GBBest valueMakes 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.
~$1,499 MSRP
RTX 4090 24GBNVIDIA upgradeMakes 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.
~$1,599 MSRP
No, Magistral Small 2507 requires more memory than RTX 5060 Ti 8GB provides.
Magistral Small 2507 (24B parameters) requires approximately 19.1 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 5060 Ti 8GB, Magistral Small 2507 achieves approximately 3.1 tokens per second decode speed with a time-to-first-token of 63282ms using Q4_K_M quantization.
For coding workloads, Magistral Small 2507 on RTX 5060 Ti 8GB receives a F grade with 3.1 tok/s and 4K context.
On RTX 5060 Ti 8GB, 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-rtx-5060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
