Magistral Small 2507 needs ~18.7 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With NVFP4 quantization, expect ~9 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
3.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.9 tok/s
TTFT
27999 ms
Safe context
4K
Memory
19.9 GB / 16.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~2.1 GB host RAM) | 7.9 tok/s | 13288 ms | 4K |
| Coding | F | Too heavy | 6.4 tok/s | 30099 ms | 4K |
| Agentic Coding | F | Too heavy | 5.4 tok/s | 52521 ms | 4K |
| Reasoning | F | Too heavy | 6.9 tok/s | 33090 ms | 4K |
| RAG | F | Too heavy | 5.4 tok/s | 65651 ms | 4K |
How Magistral Small 2507 (24B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | S93 |
Q3_K_SBest for your GPU | 3 | 11.8 GB | Low | S92 |
Copy-paste commands to run Magistral Small 2507 on your machine.
Run
ollama run magistralUpgrade 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 PRO 4000 Blackwell 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
Yes, NVIDIA T4 16GB can run Magistral Small 2507 at NVFP4 quantization (Very compromised (needs ~1.9 GB host RAM)). The recommended Q4_K_M requires 19.9 GB which exceeds available memory, but at NVFP4 it needs only 18.7 GB. Expected decode speed: 9.1 tok/s.
Magistral Small 2507 (24B parameters) requires approximately 19.9 GB at Q4_K_M quantization. On NVIDIA T4 16GB, it fits at NVFP4 using 18.7 GB.
The recommended quantization is Q4_K_M, but on NVIDIA T4 16GB the best fitting quantization is NVFP4, which uses 18.7 GB.
On NVIDIA T4 16GB, Magistral Small 2507 achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21352ms using NVFP4 quantization.
For coding workloads, Magistral Small 2507 on NVIDIA T4 16GB receives a F grade with 6.4 tok/s and 4K context.
On NVIDIA T4 16GB, Magistral Small 2507 can safely use up to 4K tokens of context at NVFP4 quantization. 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-t4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.