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Gemma 3 QAT Models
- mdp2021 2y agoAlso see # Smarter Local LLMs, Lower VRAM Costs – All Without Sacrificing Quality, Thanks to Google’s New [Quantization-Aware Training] "QAT" Optimization https://www.hardware-corner.net/smarter-local-llm-lower-vram-20250419/ https://www.hardware-corner.net/smarter-local-llm-lower-vram... > According to Google, they’ve «reduced the perplexity drop by 54% (using llama.cpp perplexity evaluation) when quantizing down to Q4_0.»
- philipkglass 2y agoAre there comparisons between int4 QAT versions of these models and the more common GGUF Q4_K_M quantizations generated post-training? The QAT models appear to be slightly larger: https://ollama.com/library/gemma3/tags https://ollama.com/library/gemma3/tags I presume QAT are better but I don't see how much better.
- mdp2021 2y ago> I presume QAT are better but I don't see how much better Not the data for Google's Gemma, but some numbers are here: https://aclanthology.org/2024.findings-acl.26/ https://aclanthology.org/2024.findings-acl.26/ ( https://aclanthology.org/2024.findings-acl.26.pdf https://aclanthology.org/2024.findings-acl.26.pdf )