Gemma 3n E2B — VRAM Requirements
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 21 lipca 2026
How much GPU VRAM you need to run Gemma 3n Gemma 3n E2B by Google DeepMind locally, a 2B-parameter model. Figures are quantized weights + KV cache + framework overhead from a memory-bandwidth roofline model.
Gemma 3n E2B needs about 2 GB VRAM at Q4_K_M.
VRAM by Quantization
| Quant | Bits/weight | Weights | Total VRAM |
|---|---|---|---|
| Q2_K | 2.63 | 0.7 GB | 1.5 GB |
| Q3_K_M | 3.41 | 0.9 GB | 1.7 GB |
| Q4_K_M | 4.83 | 1.2 GB | 2.0 GB |
| Q5_K_M | 5.67 | 1.4 GB | 2.2 GB |
| Q6_K | 6.56 | 1.6 GB | 2.4 GB |
| Q8_0 | 8.50 | 2.1 GB | 2.9 GB |
| F16 | 16.00 | 4.0 GB | 4.8 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-e2b)Estimates only — actual VRAM varies with context length, batch size, runtime and KV-cache settings.