Can I Run Gemma 4 31B on a NVIDIA RTX 4500 Ada?
Runs at Q5_K_M — good quality with reasonable headroom.
8 quantizations fit your 24.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 23.2 GB | 24.7 GB | 21.7 GB | +0.8 GB |
| Q5_K_S | 22.6 GB | 24.1 GB | 21.1 GB | +1.4 GB |
| Q4_1 | 20.6 GB | 22.1 GB | 19.1 GB | +3.4 GB |
| Q4_K_M | 20.0 GB | 21.5 GB | 18.3 GB | +4.0 GB |
| Q4_K_S | 18.9 GB | 20.4 GB | 17.4 GB | +5.1 GB |
| Q4_0 | 18.6 GB | 20.1 GB | 17.3 GB | +5.4 GB |
| Q3_K_M | 14.1 GB | 15.6 GB | 14.7 GB | +9.9 GB |
| Q3_K_S | 13.1 GB | 14.6 GB | 13.2 GB | +10.9 GB |
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Top-ranked open-source models that fit in 24.0GB.
FAQ
Can the NVIDIA RTX 4500 Ada run Gemma 4 31B?
Yes. The NVIDIA RTX 4500 Ada's 24.0GB of VRAM is enough to run Gemma 4 31B at Q5_K_M quantization (23.2GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 24.0GB. It uses about 23.2GB of memory and 24.7GB 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.