Google releases Gemma 4 QAT checkpoints for efficient on-device AI model deployment
By
Olivier Lacombe
Crusty in the right places. Worth the chew.
Summary
Google is releasing new Gemma 4 checkpoints optimized with Quantization-Aware Training (QAT) to improve model compression and efficiency. These checkpoints reduce memory requirements and enable running large language models locally on edge devices like mobile phones and laptops, as well as consumer GPUs. This follows previous Gemma 4 updates including Multi-Token Prediction (MTP) and a 12B model release.
Key quotes
· 3 pulledToday, we are releasing new checkpoints optimized with Quantization-Aware Training (QAT) to make Gemma 4 even more efficient, so you can run models locally on everyday edge devices and consumer GPUs.
By simulating quantization during training...
Since releasing Gemma 4 two months ago, we've been continuously working to expand its capabilities.
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