Mistral Small 3.2 24B Instruct 2506 needs ~19.9 GB but RTX 4070 Super 12GB only has 12.0 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
7.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.9 tok/s
TTFT
28099 ms
Safe context
4K
Memory
19.9 GB / 12.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 19.9 GB, but this setup only exposes 12.0 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 | 8.0 tok/s | 13131 ms | 4K |
| Coding | F | Too heavy | 6.9 tok/s | 28099 ms | 4K |
| Agentic Coding | F | Too heavy | 5.2 tok/s | 54018 ms | 4K |
| Reasoning | F | Too heavy | 6.9 tok/s | 33208 ms | 4K |
| RAG | F | Too heavy | 5.2 tok/s | 67523 ms | 4K |
How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 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, Mistral Small 3.2 24B Instruct 2506 requires more memory than RTX 4070 Super 12GB provides.
Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 19.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 3.2 24B Instruct 2506 is Q4_K_M, which balances quality and memory efficiency.
On RTX 4070 Super 12GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 6.9 tokens per second decode speed with a time-to-first-token of 28099ms using Q4_K_M quantization.
For coding workloads, Mistral Small 3.2 24B Instruct 2506 on RTX 4070 Super 12GB receives a F grade with 6.9 tok/s and 4K context.
On RTX 4070 Super 12GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf-on-rtx-4070-super-12gb" 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 |
| 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.
