Summary
This video pits two quantized versions of Google’s Gemma 4 12B against each other in a practical, locally-run benchmark: Google’s own Q4 GGUF produced directly from its Quantization-Aware Training (QAT) pipeline versus Unsloth’s dynamic quantization applied on top of those same QAT weights. Both files land at roughly 7 GB and start from identical base weights, making it a clean test of whether the quantization method on top of QAT actually matters for real-world output quality.
Channel host Fahd Mirza runs both models through llama.cpp on an Ubuntu machine with a 48 GB VRAM Nvidia GPU, pairing each with a Multi-Token Prediction (MTP) draft model for speed. Two non-trivial coding tasks serve as evaluation: a production-grade AWS IAM drift-detection tool in Java, and a self-contained HTML/CSS/JavaScript simulation of real-time tectonic plate movement. Results across both tasks show Google’s QAT GGUF producing cleaner code structure, clearer output, and a richer browser-rendered visualization, while Unsloth’s version generates slightly more tokens at comparable speed but with less precise results.
Viewers will come away with a concrete recommendation on which Gemma 4 12B quantization to download depending on their use case, along with practical insight into VRAM consumption (just over 10 GB for both), token-per-second speeds, and what QAT and MTP actually contribute to local inference quality.
📺 Source: Fahd Mirza · Published June 10, 2026
🏷️ Format: Comparison
