Can I Run Gemma 4 31B (free) on a NVIDIA RTX 5000 Ada?
Runs comfortably at Q6_K — minimal quality loss.
3 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 26.5 GB | 28.0 GB | 25.5 GB | +5.5 GB |
| Q5_K_M | 23.0 GB | 24.5 GB | 22.0 GB | +9.0 GB |
| Q4_K_M | 19.8 GB | 21.3 GB | 18.8 GB | +12.2 GB |
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Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the NVIDIA RTX 5000 Ada run Gemma 4 31B (free)?
Yes. The NVIDIA RTX 5000 Ada's 32.0GB of VRAM is enough to run Gemma 4 31B (free) at Q6_K quantization (26.5GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 32.0GB. It uses about 26.5GB of memory and 28.0GB 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.