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

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🔒 Hash checksum: 181d3a858b7a8817fa0ad91a7d791e86 • 📆 Last updated: 2026-06-24

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CPU: 8-core / 16-thread recommended for orchestration
RAM: minimum 16 GB for stable 8B model loading
Disk Space: 100 GB for multi-modal model vision components
Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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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