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Google releases Gemma 4 QAT models for efficient on-device inference

Google releases Gemma 4 QAT models for efficient on-device inferenceGoogle Blog (General)

New quantization-aware training checkpoints reduce Gemma 4 E2B memory to 1GB for mobile deployment. QAT minimizes quality loss compared to standard post-training quantization, enabling local inference on consumer hardware.

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8 days ago
Google releases Gemma 4 QAT models for efficient on-device inference — AIBriefs