Can I Run Gemma 4 31B (free) on a NVIDIA A100 80GB?
Runs at full precision (fp16). Zero quality loss.
5 quantizations fit your 80.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 63.0 GB | 64.5 GB | 62.0 GB | +17.0 GB |
| Q8_0 | 33.9 GB | 35.4 GB | 32.9 GB | +46.1 GB |
| Q6_K | 26.5 GB | 28.0 GB | 25.5 GB | +53.5 GB |
| Q5_K_M | 23.0 GB | 24.5 GB | 22.0 GB | +57.0 GB |
| Q4_K_M | 19.8 GB | 21.3 GB | 18.8 GB | +60.2 GB |
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Top-ranked open-source models that fit in 80.0GB.
FAQ
Can the NVIDIA A100 80GB run Gemma 4 31B (free)?
Yes. The NVIDIA A100 80GB's 80.0GB of VRAM is enough to run Gemma 4 31B (free) at fp16 quantization (63.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 80.0GB. It uses about 63.0GB of memory and 64.5GB 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.