Written by Jakub Rusinowski · Last updated September 6, 2026
How much GPU VRAM you need to run Ministral 3 Ministral 3 14B by Mistral AI locally, a 14B-parameter model. Figures are quantized weights + KV cache + framework overhead, computed from the model's parameter count and published architecture — not a throughput model. See /en/methodology.
Ministral 3 14B needs about 9 GB VRAM at Q4_K_M.
| Quant | Bits/weight | Weights | Total VRAM |
|---|---|---|---|
| Q2_K | 2.63 | 4.6 GB | 5.4 GB |
| Q3_K_M | 3.41 | 6.0 GB | 6.8 GB |
| Q4_K_M | 4.83 | 8.5 GB | 9.3 GB |
| Q5_K_M | 5.67 | 9.9 GB | 10.7 GB |
| Q6_K | 6.56 | 11.5 GB | 12.3 GB |
| Q8_0 | 8.50 | 14.9 GB | 15.7 GB |
| F16 | 16.00 | 28.0 GB | 28.8 GB |
Switch quantization in the interactive calculator, or see the full Ministral 3 model page.
or compare on Vast.ai from $0.35/hr (typical low · varies)
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Model creators: paste this into your Hugging Face model card README to link readers straight to this VRAM breakdown.
[](https://llmconfigurator.com/en/vram-calculator/ministral-3-14b?utm_source=badge&utm_medium=referral&utm_campaign=readme_badge&utm_content=ministral-3-14b)
Estimates only — actual VRAM varies with context length, batch size, runtime and KV-cache settings.