Can I Run Gemma 3 12B on a NVIDIA RTX 3080 10GB?
Runs at Q5_K_M — good quality with reasonable headroom.
10 quantizations fit your 10.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 9.7 GB | 11.2 GB | 8.4 GB | +0.3 GB |
| Q5_K_S | 9.4 GB | 10.9 GB | 8.2 GB | +0.6 GB |
| Q4_1 | 8.6 GB | 10.1 GB | 7.6 GB | +1.4 GB |
| Q4_K_M | 8.4 GB | 9.9 GB | 7.3 GB | +1.6 GB |
| Q4_K_S | 8.0 GB | 9.5 GB | 6.9 GB | +2.0 GB |
| Q4_0 | 7.9 GB | 9.4 GB | 6.9 GB | +2.1 GB |
| Q3_K_L | 6.4 GB | 7.9 GB | 6.5 GB | +3.6 GB |
| Q3_K_M | 6.1 GB | 7.6 GB | 6.0 GB | +3.9 GB |
| Q3_K_S | 5.7 GB | 7.2 GB | 5.5 GB | +4.3 GB |
| Q2_K | 5.0 GB | 6.5 GB | 4.8 GB | +5.0 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 10.0GB.
FAQ
Can the NVIDIA RTX 3080 10GB run Gemma 3 12B?
Yes. The NVIDIA RTX 3080 10GB's 10.0GB of VRAM is enough to run Gemma 3 12B at Q5_K_M quantization (9.7GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 10.0GB. It uses about 9.7GB of memory and 11.2GB 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.