Ollama

How to Install gemma-4-31B-it-qat-w4a16-ct

Deploying this model locally is quickest when done via a simple curl command.

Review and follow the instructions below.

Everything happens automatically, including the heavy cloud asset download.

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

🔗 SHA sum: 099859a5e9ade73f6a6d5ee079186993 | Updated: 2026-07-03
  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk: high-speed SSD 120 GB to cache model layers
  • Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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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