Can I Run / gemma 4 12B it / on NVIDIA A100 80GB
Can I Run gemma 4 12B it on a NVIDIA A100 80GB?
Yes
Runs at full precision (f32). Zero quality loss.
Model size
12.0B
GPU memory
80.0GB
Smallest quant
Q2_K
Best fit
f32
14 quantizations fit your 80.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 49.0 GB | 50.5 GB | 0.2 GB | +31.0 GB |
| fp16 | 25.0 GB | 26.5 GB | 0.9 GB | +55.0 GB |
| Q8_0 | 13.8 GB | 15.3 GB | 0.5 GB | +66.3 GB |
| Q6_K | 10.9 GB | 12.4 GB | 9.8 GB | +69.1 GB |
| Q5_K_M | 9.5 GB | 11.0 GB | 8.4 GB | +70.5 GB |
| Q5_K_S | 9.3 GB | 10.8 GB | 8.2 GB | +70.7 GB |
| Q4_1 | 8.5 GB | 10.0 GB | 7.4 GB | +71.5 GB |
| Q4_K_M | 8.3 GB | 9.8 GB | 7.1 GB | +71.7 GB |
| Q4_K_S | 7.9 GB | 9.4 GB | 6.8 GB | +72.1 GB |
| Q4_0 | 7.8 GB | 9.3 GB | 6.7 GB | +72.3 GB |
| Q3_K_L | 6.3 GB | 7.8 GB | 6.7 GB | +73.7 GB |
| Q3_K_M | 6.0 GB | 7.5 GB | 5.7 GB | +74.0 GB |
| Q3_K_S | 5.6 GB | 7.1 GB | 5.1 GB | +74.4 GB |
| Q2_K | 5.0 GB | 6.5 GB | 5.1 GB | +75.0 GB |
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Full model details
gemma 4 12B it →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA A100 80GB →
Top-ranked open-source models that fit in 80.0GB.
FAQ
Can the NVIDIA A100 80GB run gemma 4 12B it?
Yes. The NVIDIA A100 80GB's 80.0GB of VRAM is enough to run gemma 4 12B it at f32 quantization (49.0GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 80.0GB. It uses about 49.0GB of memory and 50.5GB 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.