Can I Run Qwen3 14B on a NVIDIA RTX 4070 Super?
Runs at Q5_K_M — good quality with reasonable headroom.
11 quantizations fit your 12.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 11.5 GB | 13.0 GB | 10.5 GB | +0.5 GB |
| Q5_K_S | 11.2 GB | 12.7 GB | 10.3 GB | +0.8 GB |
| Q5_0 | 11.2 GB | 12.7 GB | 10.3 GB | +0.8 GB |
| Q4_1 | 10.3 GB | 11.8 GB | 9.4 GB | +1.8 GB |
| Q4_K_M | 10.0 GB | 11.5 GB | 9.0 GB | +2.0 GB |
| Q4_K_S | 9.5 GB | 11.0 GB | 8.6 GB | +2.5 GB |
| Q4_0 | 9.3 GB | 10.8 GB | 8.5 GB | +2.7 GB |
| Q3_K_L | 7.6 GB | 9.1 GB | 7.9 GB | +4.4 GB |
| Q3_K_M | 7.2 GB | 8.7 GB | 7.3 GB | +4.8 GB |
| Q3_K_S | 6.7 GB | 8.2 GB | 6.7 GB | +5.3 GB |
| Q2_K | 5.9 GB | 7.4 GB | 5.8 GB | +6.1 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 Super run Qwen3 14B?
Yes. The NVIDIA RTX 4070 Super's 12.0GB of VRAM is enough to run Qwen3 14B at Q5_K_M quantization (11.5GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 12.0GB. It uses about 11.5GB of memory and 13.0GB 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.