Can I Run Mistral Small 3 on a NVIDIA RTX 4070 Ti Super?
Runs at Q4_1 — good quality with reasonable headroom.
8 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_1BEST | 15.8 GB | 17.3 GB | 14.9 GB | +0.3 GB |
| Q4_K_M | 15.3 GB | 16.8 GB | 14.3 GB | +0.7 GB |
| Q4_K_S | 14.5 GB | 16.0 GB | 13.6 GB | +1.5 GB |
| Q4_0 | 14.3 GB | 15.8 GB | 13.5 GB | +1.7 GB |
| Q3_K_L | 11.5 GB | 13.0 GB | 12.4 GB | +4.5 GB |
| Q3_K_M | 10.9 GB | 12.4 GB | 11.5 GB | +5.1 GB |
| Q3_K_S | 10.1 GB | 11.6 GB | 10.4 GB | +5.9 GB |
| Q2_K | 8.8 GB | 10.3 GB | 8.9 GB | +7.2 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 16.0GB.
FAQ
Can the NVIDIA RTX 4070 Ti Super run Mistral Small 3?
Yes. The NVIDIA RTX 4070 Ti Super's 16.0GB of VRAM is enough to run Mistral Small 3 at Q4_1 quantization (15.8GB required).
What's the best quantization to use?
Q4_1 is the highest-precision quantization that fits in your 16.0GB. It uses about 15.8GB of memory and 17.3GB 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.