How to use from the
Use from the
MLX library
# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir gemma-4-12B-Q9 inferencerlabs/gemma-4-12B-Q9

Gemma-4-12B

See gemma-4-12B in action: demonstration video

Tested on a M3 Ultra 512GB RAM using Inferencer app

  • Text inference: ~ tokens/s @ 1000 tokens ~x GiB
  • Multimodal inference: ~ tokens/s ~x GiB

9bpw quant typically achieves near lossless accuracy

Quantization (bpw)PerplexityToken AccuracyMissed Divergence
q4.51.3281290.5%26.44%
q5.51.2343795.4%16.03%
q6.51.2187596.85%12.55%
q8.51.2187597.65%9.92%
q91.2109397.95%9.61%
Base1.20312100.0%0.000%
  • Perplexity: Measures the confidence for predicting base tokens (lower is better)
  • Token Accuracy: The percentage of correctly generated base tokens
  • Missed Divergence: Measures severity of misses; how much the token was missed by
Quantized with a modified version of MLX
For more details see demonstration video or visit google/gemma-4-12B-it.

Disclaimer

We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.

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