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

Follow the guidelines below to continue.

The setup auto-streams the model assets (expect a multi-GB download).

The installer will automatically analyze your hardware and select the optimal configuration for your system.

???? Hash sum → 107180401d04535bd5072f49c1a620ba — Update date: 2026-06-24 Verify Processor: next-gen chip for heavy context processing RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: at least 100 GB for multiple local LLM variants GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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