Setup gemma-4-31B-it-qat-w4a16-ct with Native FP4

For an instant local deployment, running a pre-configured shell script is ideal.

Kindly follow the on-screen instructions below.

Hands-free setup: the system self-downloads the heavy model files.

You don’t need to tweak anything; the installer picks the highest performing setup.

📤 Release Hash: d25170625d4673b25ea952ce08190b46 • 📅 Date: 2026-06-28 CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 48 GB needed to prevent memory swapping to disk Disk Space: at least 100 GB for multiple local LLM variants GPU: high memory bandwidth GPU for next-gen local AI pipeline

The Gemma-4-31B-it-qat-w4a16-ct is a large language model designed for instruction following and conversational tasks. It leverages 31 billion parameters to achieve a balance between accuracy and computational efficiency. The model employs QAT (quantized aware training) combined with a w4a16 format, enabling reduced memory footprint while preserving performance. Its CT architecture incorporates advanced attention mechanisms that improve context retention and response relevance. The following table summarizes key technical attributes.

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