Can I Run / Ministral 3 8B / on NVIDIA RTX 2070 Super
Can I Run Ministral 3 8B on a NVIDIA RTX 2070 Super?
Yes
Runs comfortably at Q6_K — minimal quality loss.
Model size
8.0B
GPU memory
8.0GB
Smallest quant
Q4_K_M
Best fit
Q6_K
3 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 7.6 GB | 9.1 GB | 6.6 GB | +0.4 GB |
| Q5_K_M | 6.7 GB | 8.2 GB | 5.7 GB | +1.3 GB |
| Q4_K_M | 5.8 GB | 7.3 GB | 4.8 GB | +2.2 GB |
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Full model details
Ministral 3 8B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA RTX 2070 Super →
Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the NVIDIA RTX 2070 Super run Ministral 3 8B?
Yes. The NVIDIA RTX 2070 Super's 8.0GB of VRAM is enough to run Ministral 3 8B at Q6_K quantization (7.6GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 8.0GB. It uses about 7.6GB of memory and 9.1GB 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.