Mistral Nemo 12B needs ~11.6 GB but GTX 1060 6GB only has 6.0 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
5.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.9 tok/s
TTFT
67365 ms
Safe context
4K
Memory
11.6 GB / 6.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 11.6 GB, but this setup only exposes 6.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 3.7 tok/s | 28759 ms | 4K |
| Coding | F | Too heavy | 2.7 tok/s | 72417 ms | 4K |
| Agentic Coding | F | Too heavy | 2.5 tok/s | 112847 ms | 4K |
| Reasoning | F | Too heavy | 2.9 tok/s | 79613 ms | 4K |
| RAG | F | Too heavy | 2.5 tok/s | 141059 ms | 4K |
How Mistral Nemo 12B (12B params) fits at each quantization level on GTX 1060 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | F0 |
Q3_K_S | 3 | 5.9 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX 3060 12GBBudget 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.
~$329 MSRP
RTX 5060 Ti 16GBBest 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.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA 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.
~$499 MSRP
No, Mistral Nemo 12B requires more memory than GTX 1060 6GB provides.
Mistral Nemo 12B (12B parameters) requires approximately 11.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Nemo 12B is Q4_K_M, which balances quality and memory efficiency.
On GTX 1060 6GB, Mistral Nemo 12B achieves approximately 2.7 tokens per second decode speed with a time-to-first-token of 72417ms using Q4_K_M quantization.
For coding workloads, Mistral Nemo 12B on GTX 1060 6GB receives a F grade with 2.7 tok/s and 4K context.
On GTX 1060 6GB, Mistral Nemo 12B can safely use up to 4K tokens of context. The model's official context limit is 128K, 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/mistral-nemo-12b-on-gtx-1060-6gb" 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 | 7.3 GB | Medium | F0 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 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.
