Ministral 3 14B needs ~14.5 GB but RTX 2080 Ti 11GB only has 11.0 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
3.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
16.1 tok/s
TTFT
12040 ms
Safe context
4K
Memory
14.5 GB / 11.0 GB
Offload
20%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 14.5 GB, but this setup only exposes 11.0 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 | 18.1 tok/s | 5819 ms | 4K |
| Coding | F | Too heavy | 15.0 tok/s | 12943 ms | 4K |
| Agentic Coding | F | Too heavy | 10.6 tok/s | 26507 ms | 4K |
| Reasoning | F | Too heavy | 15.0 tok/s | 15297 ms | 4K |
| RAG | F | Too heavy | 10.6 tok/s | 33134 ms | 4K |
How Ministral 3 14B (14B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | S88 |
Q3_K_S | 3 | 6.9 GB | Low | S87 |
NVFP4Best for your GPU |
Upgrade options
RTX 5060 Ti 16GBBudget 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.
~$449 MSRP
RTX 4060 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.
~$499 MSRP
RTX 2000 Ada 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.
~$625 MSRP
RTX 4090 24GBBiggest leapMakes 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, Ministral 3 14B requires more memory than RTX 2080 Ti 11GB provides.
Ministral 3 14B (14B parameters) requires approximately 14.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 3 14B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2080 Ti 11GB, Ministral 3 14B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12943ms using Q4_K_M quantization.
For coding workloads, Ministral 3 14B on RTX 2080 Ti 11GB receives a F grade with 15.0 tok/s and 4K context.
On RTX 2080 Ti 11GB, Ministral 3 14B can safely use up to 4K tokens of context. The model's official context limit is 262K, 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/ministral-3-14b-on-rtx-2080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
7.8 GB |
| Medium |
| S87 |
Q4_K_M | 4 | 8.5 GB | Medium | F0 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 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.
