Can I Run Ministral 3 14B 2512 on a NVIDIA RTX 3080 Ti?
Runs at Q5_K_M — good quality with reasonable headroom.
9 quantizations fit your 12.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 10.9 GB | 12.4 GB | 9.6 GB | +1.1 GB |
| Q5_K_S | 10.6 GB | 12.1 GB | 9.4 GB | +1.4 GB |
| Q4_1 | 9.7 GB | 11.2 GB | 8.6 GB | +2.3 GB |
| Q4_K_M | 9.4 GB | 10.9 GB | 8.2 GB | +2.6 GB |
| Q4_K_S | 9.0 GB | 10.5 GB | 7.8 GB | +3.0 GB |
| Q4_0 | 8.8 GB | 10.3 GB | 7.8 GB | +3.2 GB |
| Q3_K_M | 6.8 GB | 8.3 GB | 6.7 GB | +5.2 GB |
| Q3_K_S | 6.3 GB | 7.8 GB | 6.1 GB | +5.7 GB |
| Q2_K | 5.6 GB | 7.1 GB | 5.3 GB | +6.4 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 12.0GB.
FAQ
Can the NVIDIA RTX 3080 Ti run Ministral 3 14B 2512?
Yes. The NVIDIA RTX 3080 Ti's 12.0GB of VRAM is enough to run Ministral 3 14B 2512 at Q5_K_M quantization (10.9GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 12.0GB. It uses about 10.9GB of memory and 12.4GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.