Mistral Nemo 12B needs ~11.6 GB but RTX 4050 Laptop 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
3.9 tok/s
TTFT
49883 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.
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 | 4.9 tok/s | 21513 ms | 4K |
| Coding | F | Too heavy | 3.9 tok/s | 49883 ms | 4K |
| Agentic Coding | F | Too heavy | 3.1 tok/s | 91205 ms | 4K |
| Reasoning | F | Too heavy | 3.9 tok/s | 58953 ms | 4K |
| RAG | F | Too heavy | 3.1 tok/s | 114007 ms | 4K |
How Mistral Nemo 12B (12B params) fits at each quantization level on RTX 4050 Laptop 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 RTX 4050 Laptop 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 RTX 4050 Laptop 6GB, Mistral Nemo 12B achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49883ms using Q4_K_M quantization.
For coding workloads, Mistral Nemo 12B on RTX 4050 Laptop 6GB receives a F grade with 3.9 tok/s and 4K context.
On RTX 4050 Laptop 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-rtx-4050-laptop-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.
