Can I Run / gemma 4 12B it / on NVIDIA RTX 5070
Can I Run gemma 4 12B it on a NVIDIA RTX 5070?
Yes
Runs comfortably at Q6_K — minimal quality loss.
Model size
12.0B
GPU memory
12.0GB
Smallest quant
Q2_K
Best fit
Q6_K
11 quantizations fit your 12.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 10.9 GB | 12.4 GB | 9.8 GB | +1.1 GB |
| Q5_K_M | 9.5 GB | 11.0 GB | 8.4 GB | +2.5 GB |
| Q5_K_S | 9.3 GB | 10.8 GB | 8.2 GB | +2.7 GB |
| Q4_1 | 8.5 GB | 10.0 GB | 7.4 GB | +3.5 GB |
| Q4_K_M | 8.3 GB | 9.8 GB | 7.1 GB | +3.7 GB |
| Q4_K_S | 7.9 GB | 9.4 GB | 6.8 GB | +4.1 GB |
| Q4_0 | 7.8 GB | 9.3 GB | 6.7 GB | +4.3 GB |
| Q3_K_L | 6.3 GB | 7.8 GB | 6.7 GB | +5.7 GB |
| Q3_K_M | 6.0 GB | 7.5 GB | 5.7 GB | +6.0 GB |
| Q3_K_S | 5.6 GB | 7.1 GB | 5.1 GB | +6.4 GB |
| Q2_K | 5.0 GB | 6.5 GB | 5.1 GB | +7.0 GB |
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Full model details
gemma 4 12B it →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA RTX 5070 →
Top-ranked open-source models that fit in 12.0GB.
FAQ
Can the NVIDIA RTX 5070 run gemma 4 12B it?
Yes. The NVIDIA RTX 5070's 12.0GB of VRAM is enough to run gemma 4 12B it at Q6_K quantization (10.9GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 12.0GB. It uses about 10.9GB of memory and 12.4GB 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.