Gemma 3n E4B — VRAM Requirements
Written by Jakub Rusinowski · Last updated April 1, 2025
How much GPU VRAM you need to run Gemma 3n Gemma 3n E4B by Google DeepMind locally, a 7.85B-parameter model. Figures are quantized weights + KV cache + framework overhead, computed from the model's parameter count and published architecture — not a throughput model. See /en/methodology.
Gemma 3n E4B needs about 6 GB VRAM at Q4_K_M.
VRAM by Quantization
| Quant | Bits/weight | Weights | Total VRAM |
|---|---|---|---|
| Q2_K | 2.63 | 2.6 GB | 3.4 GB |
| Q3_K_M | 3.41 | 3.3 GB | 4.1 GB |
| Q4_K_M | 4.83 | 4.7 GB | 5.5 GB |
| Q5_K_M | 5.67 | 5.6 GB | 6.4 GB |
| Q6_K | 6.56 | 6.4 GB | 7.2 GB |
| Q8_0 | 8.50 | 8.3 GB | 9.1 GB |
| F16 | 16.00 | 15.7 GB | 16.5 GB |
Switch quantization in the interactive calculator, or see the full Gemma 3n model page.
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Model creators: paste this into your Hugging Face model card README to link readers straight to this VRAM breakdown.
[](https://llmconfigurator.com/en/vram-calculator/gemma-3n-e4b?utm_source=badge&utm_medium=referral&utm_campaign=readme_badge&utm_content=gemma-3n-e4b)Estimates only — actual VRAM varies with context length, batch size, runtime and KV-cache settings.