Google QAT vs. Unsloth QAT: Gemma 4 12B Performance Comparison
Generated: 2026-06-10 · API: Gemini 2.5 Flash · Modes: Summary
Google QAT vs. Unsloth QAT: Gemma 4 12B Performance Comparison
Clip title: Google QAT vs Unsloth QAT + MTP - Which Gemma 4 12B Is Actually Better? Author / channel: Fahd Mirza URL: https://www.youtube.com/watch?v=pMhiJ8CzszI
Summary
This video provides a comprehensive comparison between two quantized versions of Google’s Gemma 4 12B model: Google’s own QAT Q4_0 and Unsloth’s UD-Q4_K_XL. The main objective is to determine which quantization method performs better, especially when the foundational weights are already Quantity Aware Training (QAT) optimized. QAT involves training the model to withstand compression, dramatically reducing quality loss compared to standard post-training methods, effectively “baking in” quantization. The comparison also briefly touches on Multi-Token Prediction (MTP), a companion model that drafts several tokens ahead for verification by the main model, enabling double the inference speed without compromising output quality. Both QAT models compared in the video are approximately 7GB in size, highlighting that the difference lies in their approach to quantization rather than model scale.
The comparison involved three distinct tasks run on a local Ubuntu system with an NVIDIA RTX A6000 GPU using the llama.cpp server. The first task was to build a “production-grade cloud IAM drift fixer” tool. Google’s QAT model produced a well-structured and organized project, effectively identifying critical administrative privilege escalation and high-risk wildcard resource permissions, along with clear remediation patches. While Unsloth’s QAT model also detected similar issues and generated remediation, its output structure and clarity were less defined compared to Google’s version, giving Google a clear edge in this backend engineering task.
For the second task, a complex frontend development challenge, the models were asked to build a self-contained HTML file simulating real-time tectonic plate movements on Earth. Both models generated the necessary HTML, CSS, and JavaScript. While Unsloth’s version produced a basic, non-interactive simulation with simple polygons and a dashboard, Google’s output was significantly richer and more detailed. Google’s simulation displayed interactive elements, such as “cracks” appearing upon clicking, real-time seismic waveforms, GPS sensor readings, and event logs, demonstrating a much higher quality of code and a more sophisticated understanding of the complex requirements.
The final two tasks involved creative writing and multilingual generation. For creative writing, both models were prompted to write an insightful non-fiction paragraph about an immigrant living alone, using WhatsApp messages as a core theme. While subjective, the presenter found Google’s response to possess greater philosophical depth and coherence, particularly in lines that explored modern alienation, even if Unsloth’s language was slightly cleaner. Lastly, for the multilingual test, both models were asked to generate original motivational quotes in 80 different languages. Both performed remarkably well, delivering unique and culturally appropriate quotes for each language, with Google generating one more quote.
In conclusion, across the diverse set of tasks, Google’s Gemma 4 12B QAT Q4_0 generally demonstrated superior performance, particularly in terms of output quality, clarity, and depth, especially in complex coding and creative writing scenarios. While Unsloth’s dynamic quantization method on the QAT base also performed commendably, offering good speed and relatively clean output, it often lacked the richness, detail, and philosophical nuance that Google’s directly trained QAT model consistently delivered. The results suggest that for demanding applications requiring highly coherent and detailed responses, Google’s proprietary QAT optimization maintains a significant advantage.
Video Description & Links
Description
Both use the same QAT base weights and the same MTP setup, so the only variable is the quantization method, and the results are closer than you think.
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RESOURCES:
▶ https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF ▶ https://huggingface.co/google/gemma-4-12B-it-qat-q4_0-gguf
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