01What it can do
- Full multimodal support in ~3 GB of Q4 VRAM (4.3 GB of Ollama weights in QAT)
- Runs on CPU or entry-level GPU
- 128k context
- Thinking mode can be enabled
- Apache 2.0 (free for commercial use)
- —Quality below the E4B and 26B
- —Reasoning benchmarks: limited models vs. larger models
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for Gemma 4 E2B?
To run Gemma 4 E2B locally with Q4 quantization, you need about 3 GB of VRAM. An option to compare: RTX 5060 Ti 16GB (ASUS Prime) — leave some headroom for the system and context; check engine compatibility with the GPU.
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On the go: Gemma 4 E2B also runs on a RTX laptop PC (16 GB of VRAM) →
Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- Lifetime updates
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.