The most efficient approach for a local installation is leveraging Docker containers.
Check out the detailed setup guide below to begin.
The script takes care of fetching the multi-gigabyte model weights.
The engine benchmarks your hardware to apply the most effective operational mode.
The **gemma-4-12B-it-QAT-GGUF** model is a 12‑billion parameter instruction‑tuned language model designed for high performance and efficiency. It leverages *QAT* (quantized aware training) and the GGUF format to achieve a *balanced trade‑off* between accuracy and inference speed on consumer hardware. The model supports a context window of up to **8192** tokens, enabling it to understand and generate longer passages with coherent reasoning. Benchmarks show it outperforms comparable open models in reasoning and coding tasks while maintaining a modest memory footprint. Below is a quick comparison of its core specifications to illustrate how it stands against other popular open models:
| Spec | Value |
|---|---|
| Parameters | **12 B** |
| Context Length | **8192** tokens |
| Quantization | QAT‑GGUF |
| Benchmark (MMLU) | 68% |
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