Can I Run Ministral 3 3B 2512 on a NVIDIA RTX 4070?
Runs at full precision (fp16). Zero quality loss.
12 quantizations fit your 12.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 8.6 GB | 10.1 GB | 0.8 GB | +3.4 GB |
| Q8_0 | 5.0 GB | 6.5 GB | 3.6 GB | +7.0 GB |
| Q6_K | 4.1 GB | 5.6 GB | 2.8 GB | +7.9 GB |
| Q5_K_M | 3.7 GB | 5.2 GB | 2.5 GB | +8.3 GB |
| Q5_K_S | 3.6 GB | 5.1 GB | 2.4 GB | +8.4 GB |
| Q4_1 | 3.4 GB | 4.9 GB | 2.2 GB | +8.6 GB |
| Q4_K_M | 3.3 GB | 4.8 GB | 2.1 GB | +8.7 GB |
| Q4_K_S | 3.2 GB | 4.7 GB | 2.0 GB | +8.8 GB |
| Q4_0 | 3.1 GB | 4.6 GB | 2.0 GB | +8.9 GB |
| Q3_K_M | 2.6 GB | 4.1 GB | 1.8 GB | +9.4 GB |
| Q3_K_S | 2.5 GB | 4.0 GB | 1.6 GB | +9.5 GB |
| Q2_K | 2.3 GB | 3.8 GB | 1.5 GB | +9.8 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 4070 run Ministral 3 3B 2512?
Yes. The NVIDIA RTX 4070's 12.0GB of VRAM is enough to run Ministral 3 3B 2512 at fp16 quantization (8.6GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 12.0GB. It uses about 8.6GB of memory and 10.1GB 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.