Setup gemma-4-31B-it-qat-w4a16-ct Locally via LM Studio Dummy Proof Guide

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

Follow the sequence of steps detailed below.

Everything happens automatically, including the heavy cloud asset download.

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

📘 Build Hash: b2f136ab5289ffbb32bf695b721c13e5 • 🗓 2026-06-29 Verify Processor: 6-core 3.5 GHz minimum required RAM: 48 GB needed to prevent memory swapping to disk Storage: extra room for future model updates and datasets GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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