Can I Run gemma 4 12B it on a NVIDIA A100 40GB?
Runs at full precision (fp16). Zero quality loss.
13 quantizations fit your 40.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 25.0 GB | 26.5 GB | 0.9 GB | +15.0 GB |
| Q8_0 | 13.8 GB | 15.3 GB | 0.5 GB | +26.3 GB |
| Q6_K | 10.9 GB | 12.4 GB | 9.8 GB | +29.1 GB |
| Q5_K_M | 9.5 GB | 11.0 GB | 8.4 GB | +30.5 GB |
| Q5_K_S | 9.3 GB | 10.8 GB | 8.2 GB | +30.7 GB |
| Q4_1 | 8.5 GB | 10.0 GB | 7.4 GB | +31.5 GB |
| Q4_K_M | 8.3 GB | 9.8 GB | 7.1 GB | +31.7 GB |
| Q4_K_S | 7.9 GB | 9.4 GB | 6.8 GB | +32.1 GB |
| Q4_0 | 7.8 GB | 9.3 GB | 6.7 GB | +32.3 GB |
| Q3_K_L | 6.3 GB | 7.8 GB | 6.7 GB | +33.7 GB |
| Q3_K_M | 6.0 GB | 7.5 GB | 5.7 GB | +34.0 GB |
| Q3_K_S | 5.6 GB | 7.1 GB | 5.1 GB | +34.4 GB |
| Q2_K | 5.0 GB | 6.5 GB | 5.1 GB | +35.0 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 40.0GB.
FAQ
Can the NVIDIA A100 40GB run gemma 4 12B it?
Yes. The NVIDIA A100 40GB's 40.0GB of VRAM is enough to run gemma 4 12B it at fp16 quantization (25.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 40.0GB. It uses about 25.0GB of memory and 26.5GB 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.