Anyone seen benchmarks comparing Gemma 4 4-bit QAT vs. 8-bit standard quants?
A Reddit user asks for direct benchmarks comparing Gemma 4 4-bit QAT with standard 8-bit quants.
A r/LocalLLaMA user is looking for benchmarks comparing Gemma 4 4-bit QAT models, via Unsloth, against standard 8-bit non-QAT quantized models. They understand QAT is expected to preserve much of the BF16 baseline accuracy, but want hard numbers against traditional 8-bit PTQ. The post highlights scattered feedback but no clear head-to-head evaluation yet.
這篇 r/LocalLLaMA 貼文不是發布新模型或正式基準，而是社群中的一個實務問題：作者正在尋找是否有人已經針對 Gemma 4 的 4-bit QAT 模型，特別是透過 Unsloth 取得或使用的版本，與傳統 8-bit 非 QAT 量化模型做過直接比較。問題核心在於，QAT（Quantization-Aware Training，量化感知訓練）通常被認為比一般訓練後量化 PTQ 更能保留原始 BF16 模型的準確度，因此 4-bit QAT 雖然位元數更低、理論上更省記憶體與推論資源，仍可能在品質上接近高精度模型。
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