Google
Gemma 4 E4B (8B parameters) requires approximately 8.0 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 10 GB of VRAM.
Get started
— copy & paste to run locallyCopy-paste commands to run Gemma 4 E4B on your machine.
Run
ollama run gemma4:e4bQuick specs
About this model
Related models
Quick picks
Best hardware
Run this model
Quantization options
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | — |
Q3_K_S | 3 | 3.9 GB | Low | — |
NVFP4 | 4 | 4.5 GB | Medium | — |
Q4_K_M | 4 | 4.9 GB | Medium | — |
Q5_K_M | 5 | 5.8 GB | High | — |
Q6_K | 6 | 6.6 GB | High | — |
Q8_0 | 8 | 8.6 GB | Very High | — |
F16 | 16 | 16.4 GB | Maximum | — |
Quality benchmarks
Coding
Reasoning
Source: official · 2026-04-02
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Gemma 4 E4B (8B parameters) requires approximately 8.0 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc B570 10GB can run Gemma 4 E4B with a compatibility score of 81/100. It provides 10 GB of memory and achieves approximately 34.3 tokens per second.
The recommended quantization for Gemma 4 E4B 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 E4B: RTX 3080 Ti 12GB (score: 84/100), RTX 5070 12GB (score: 84/100), RTX 3080 12GB (score: 84/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Gemma 4 E4B is well-suited for chat as well as reasoning, multimodal, coding. It was designed with these use cases in mind.
See also
