The most efficient approach for a local installation is leveraging Docker containers.
Use the instructions provided below to complete the setup.
1-click setup: the app automatically fetches the large weight files.
The automated script takes care of everything, tailoring the setup to your specs.
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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