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Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.4 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 7 GB of VRAM.
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ollama run gemma4:e2bQuick specs
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.0 GB | Low | — |
Q3_K_S | 3 | 2.5 GB | Low | — |
NVFP4 | 4 | 2.9 GB | Medium | — |
Q4_K_M | 4 | 3.1 GB | Medium | — |
Q5_K_M | 5 | 3.7 GB | High | — |
Q6_K | 6 | 4.2 GB | High | — |
Q8_0 | 8 | 5.5 GB | Very High | — |
F16 | 16 | 10.5 GB | Maximum | — |
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Coding
Reasoning
Source: official · 2026-04-02
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.4 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc A380 6GB can run Gemma 4 E2B with a compatibility score of 72/100. It provides 6 GB of memory and achieves approximately 24.1 tokens per second.
The recommended quantization for Gemma 4 E2B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for Gemma 4 E2B: RTX 3060 Ti 8GB (score: 79/100), RTX 3070 8GB (score: 79/100), RTX 3000 Ada Laptop 8GB (score: 79/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Gemma 4 E2B is well-suited for chat as well as reasoning, multimodal, coding. It was designed with these use cases in mind.
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