Can I Run / Ministral 3 3B / on NVIDIA RTX 2070 Super
Can I Run Ministral 3 3B on a NVIDIA RTX 2070 Super?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
3.0B
GPU memory
8.0GB
Smallest quant
Q4_K_M
Best fit
fp16
5 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 7.0 GB | 8.5 GB | 6.0 GB | +1.0 GB |
| Q8_0 | 4.2 GB | 5.7 GB | 3.2 GB | +3.8 GB |
| Q6_K | 3.5 GB | 5.0 GB | 2.5 GB | +4.5 GB |
| Q5_K_M | 3.1 GB | 4.6 GB | 2.1 GB | +4.9 GB |
| Q4_K_M | 2.8 GB | 4.3 GB | 1.8 GB | +5.2 GB |
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Full model details
Ministral 3 3B →
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 3B?
Yes. The NVIDIA RTX 2070 Super's 8.0GB of VRAM is enough to run Ministral 3 3B at fp16 quantization (7.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 8.0GB. It uses about 7.0GB of memory and 8.5GB 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.