Can I Run / Gemma 4 31B (free) / on NVIDIA A100 40GB
Can I Run Gemma 4 31B (free) on a NVIDIA A100 40GB?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
31.0B
GPU memory
40.0GB
Smallest quant
Q4_K_M
Best fit
Q8_0
4 quantizations fit your 40.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 33.9 GB | 35.4 GB | 32.9 GB | +6.1 GB |
| Q6_K | 26.5 GB | 28.0 GB | 25.5 GB | +13.5 GB |
| Q5_K_M | 23.0 GB | 24.5 GB | 22.0 GB | +17.0 GB |
| Q4_K_M | 19.8 GB | 21.3 GB | 18.8 GB | +20.2 GB |
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Full model details
Gemma 4 31B (free) →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA A100 40GB →
Top-ranked open-source models that fit in 40.0GB.
FAQ
Can the NVIDIA A100 40GB run Gemma 4 31B (free)?
Yes. The NVIDIA A100 40GB's 40.0GB of VRAM is enough to run Gemma 4 31B (free) at Q8_0 quantization (33.9GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 40.0GB. It uses about 33.9GB of memory and 35.4GB 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.