Can I Run / Gemma 4 E2B / on NVIDIA A100 40GB
Can I Run Gemma 4 E2B on a NVIDIA A100 40GB?
Yes
Runs at full precision (f32). Zero quality loss.
Model size
5.1B
GPU memory
40.0GB
Smallest quant
Q2_K
Best fit
f32
14 quantizations fit your 40.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 21.4 GB | 22.9 GB | 1.9 GB | +18.6 GB |
| fp16 | 11.2 GB | 12.7 GB | 0.2 GB | +28.8 GB |
| Q8_0 | 6.4 GB | 7.9 GB | 0.1 GB | +33.6 GB |
| Q6_K | 5.2 GB | 6.7 GB | 4.5 GB | +34.8 GB |
| Q5_K_M | 4.6 GB | 6.1 GB | 3.4 GB | +35.4 GB |
| Q5_K_S | 4.5 GB | 6.0 GB | 3.3 GB | +35.5 GB |
| Q4_1 | 4.2 GB | 5.7 GB | 3.1 GB | +35.8 GB |
| Q4_K_M | 4.1 GB | 5.6 GB | 3.1 GB | +35.9 GB |
| Q4_K_S | 3.9 GB | 5.4 GB | 3.0 GB | +36.1 GB |
| Q4_0 | 3.9 GB | 5.4 GB | 3.0 GB | +36.1 GB |
| Q3_K_L | 3.3 GB | 4.8 GB | 3.3 GB | +36.7 GB |
| Q3_K_M | 3.1 GB | 4.6 GB | 2.5 GB | +36.9 GB |
| Q3_K_S | 3.0 GB | 4.5 GB | 2.5 GB | +37.0 GB |
| Q2_K | 2.7 GB | 4.2 GB | 3.0 GB | +37.3 GB |
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Full model details
Gemma 4 E2B →
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 E2B?
Yes. The NVIDIA A100 40GB's 40.0GB of VRAM is enough to run Gemma 4 E2B at f32 quantization (21.4GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 40.0GB. It uses about 21.4GB of memory and 22.9GB 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.