LocalLLaMA User Weighs QAT Gemma 31B GGUF Quants for RTX 3060
Original: Are these quants of QAT better than non-QAT? What do I use?
A LocalLLaMA user asks whether new QAT Gemma 31B GGUF quants outperform older non-QAT options on 12GB VRAM.
A Reddit user with an RTX 3060 12GB and 32GB DDR3 RAM is evaluating new QAT-based Gemma 31B GGUF quantizations. They currently run an older Unsloth Gemma 31B IQ3_XXS build at long context, with some tensor and mmproj offloading to CPU. The post asks which Q2-Q3 quant to choose, whether QAT changes quality expectations, and whether MTP would help or hurt under tight VRAM limits.
This LocalLLaMA post is a practical hardware-and-quantization question rather than a release announcement or benchmark. The author is trying to decide whether to move from an older Unsloth Gemma 31B instruction-tuned GGUF quant to newer QAT-derived Gemma 31B GGUF files hosted on Hugging Face. The central issue is whether quantization-aware training, or QAT, makes very low-bit quants meaningfully better than non-QAT quantizations for local inference on constrained consumer hardware.
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