How to Setup gemma-4-E4B-it Locally via Ollama 2 with Native FP4

How to Setup gemma-4-E4B-it Locally via Ollama 2 with Native FP4

The fastest way to get this model running locally is via Docker.

Refer to the instructions below to proceed.

Next, run the Docker command to spin up the container.

📡 Hash Check: 23a6781c34657ff6e1d08e38da00bf30 | 📅 Last Update: 2026-06-25



  • CPU: modern architecture (Zen 3 / Alder Lake minimum)
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk Space: 100 GB for multi-modal model vision components
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

Gemma-4-E4B-it is a state‑of‑the‑art language model engineered for high‑efficiency inference on edge devices. It incorporates 2 B parameters and a 4 K context window, allowing nuanced comprehension while preserving low latency. The architecture leverages advanced quantization techniques to achieve sub‑2 ms token generation on consumer hardware. Its design includes multi‑head attention and grouped‑query attention, delivering strong performance across benchmarks such as MMLU and GSM‑8K. The model also supports seamless integration with developer tools through its open‑source API.

Parameters 2 B
Context Length 4 K tokens
Quantization INT4
Throughput >2000 tokens/s on GPU
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