Can I Run / gemma 4 E4B it / on NVIDIA A100 80GB
Can I Run gemma 4 E4B it on a NVIDIA A100 80GB?
Yes
Runs at full precision (f32). Zero quality loss.
Model size
8.0B
GPU memory
80.0GB
Smallest quant
Q3_K_S
Best fit
f32
12 quantizations fit your 80.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 33.0 GB | 34.5 GB | 1.9 GB | +47.0 GB |
| fp16 | 17.0 GB | 18.5 GB | 1.0 GB | +63.0 GB |
| Q8_0 | 9.5 GB | 11.0 GB | 8.2 GB | +70.5 GB |
| Q6_K | 7.6 GB | 9.1 GB | 7.1 GB | +72.4 GB |
| Q5_K_M | 6.7 GB | 8.2 GB | 5.5 GB | +73.3 GB |
| Q5_K_S | 6.5 GB | 8.0 GB | 5.4 GB | +73.5 GB |
| Q4_1 | 6.0 GB | 7.5 GB | 5.1 GB | +74.0 GB |
| Q4_K_M | 5.8 GB | 7.3 GB | 5.0 GB | +74.2 GB |
| Q4_K_S | 5.6 GB | 7.1 GB | 4.8 GB | +74.4 GB |
| Q4_0 | 5.5 GB | 7.0 GB | 4.8 GB | +74.5 GB |
| Q3_K_M | 4.3 GB | 5.8 GB | 4.1 GB | +75.7 GB |
| Q3_K_S | 4.1 GB | 5.6 GB | 3.9 GB | +75.9 GB |
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Full model details
gemma 4 E4B 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 E4B it?
Yes. The NVIDIA A100 80GB's 80.0GB of VRAM is enough to run gemma 4 E4B it at f32 quantization (33.0GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 80.0GB. It uses about 33.0GB of memory and 34.5GB 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.