gemma-4-31B-it-qat-w4a16-ct on AMD/Nvidia GPU Quantized GGUF Easy Build

Running this model locally is fastest when deployed through a PowerShell script.

Make sure you implement the steps mentioned below.

The installer automatically pulls the model (could be multiple GBs).

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

💾 File hash: aebc175ee5dee0fac7fe1ca849381861 (Update date: 2026-06-24)
  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Disk Space: 100 GB for multi-modal model vision components
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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.

Parameter Count 31 B
Quantization QAT (w4a16)
Precision 16‑bit float
Training Method Instruction‑following fine‑tuning
Architecture CT with enhanced attention
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  • Downloader for specialized named entity recognition model files
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