gemma-4-31B-it-NVFP4-turbo-GGUF

<div align="center"> <img src="https://huggingface.co/LilaRest/gemma-4-31B-it-NVFP4-turbo/resolve/main/banner.png"> </div> <h1 align="center">⚡ Gemma 4 31B IT NVFP4 <i>Turbo</i> GGUF</h1>

Requires ggml-org/llama.cpp#21971

A repackaged nvidia/Gemma-4-31B-IT-NVFP4 that is 68% smaller in GPU memory and ~2.5× faster than the base model, while retaining nearly identical quality (1-3% loss). Fits on a single RTX 5090 (🎉).

Approach

Three changes were made:

1. Quantized all self-attention weights from BF16 → FP4 (RTN, group_size=16, matching modelopt NVFP4 format)
2. Updated architecture to Gemma4ForCausalLM and quantization config accordingly
3. Stripped the vision and audio encoder

Everything else is untouched — MLP layers keep NVIDIA's calibrated FP4, embed_tokens stays BF16, all norms preserved, so we retain all the nvidia/Gemma-4-31B-IT-NVFP4 optimizations.

Why RTN didn't hurt quality

RTN (Round-To-Nearest) is the simplest quantization method — no calibration data, fully reproducible. It worked here because:

• FP4 with group_size=16 and per-group scaling preserves relative weight distributions well
• Self-attention weights tend to be normally distributed near zero, where the FP4 grid has finest resolution (0, 0.5, 1.0, 1.5)
• MLP layers (more sensitive to quantization) keep NVIDIA's calibrated FP4
• embed_tokens stays BF16, preventing noise from propagating through all layers

License

Apache 2.0 — same as the base model.

Credits

• Google DeepMind for Gemma 4
• NVIDIA for the modelopt NVFP4 checkpoint