Launch gemma-4-31B-it-qat-w4a16-ct

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

Refer to the instructions below to proceed.

The script takes care of fetching the multi-gigabyte model weights.

During setup, the script automatically determines and applies the best settings.

📊 File Hash: 99d23356d5f442a8d9885bf09829fb6d — Last update: 2026-07-03

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Processor: next-gen chip for heavy context processing
RAM: 32 GB highly recommended for 26B+ GGUF models
Disk Space: at least 100 GB for multiple local LLM variants
Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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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Launch gemma-4-31B-it-qat-w4a16-ct

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