Best Gemma 4 GGUFs: Evaluations from Q4 to Q2

2026-04-06 · Source: The Kaitchup – AI on a Budget · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Google recently released Gemma 4, which includes 31B and 26B parameter models featuring an A4B architecture. Following this release, the open-source community rapidly developed GGUF versions of these models. These GGUF variants enable local execution using tools such as LM Studio and llama.cpp, significantly reducing GPU memory requirements compared to the original models. Despite their availability and potential for broader accessibility, these community-produced GGUF versions have not yet undergone proper evaluation to assess their performance or fidelity.

Key takeaway

For NLP engineers considering local deployment of Google's Gemma 4 models, you should proceed with caution regarding the community-produced GGUF versions. While they offer significant memory savings for local inference, their lack of formal evaluation means their performance and reliability are currently unverified. Prioritize independent testing or await official benchmarks before integrating them into critical workflows.

Key insights

Gemma 4 GGUF versions enable local execution with reduced GPU memory, but lack formal evaluation.

In practice

• Run Gemma 4 locally via LM Studio.
• Utilize llama.cpp for Gemma 4 inference.

Topics

• Gemma 4
• GGUF
• Open-source Models
• Local Inference
• LM Studio

Best for: NLP Engineer, Machine Learning Engineer, AI Engineer

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• New DiffusionGemma and MoQ GGUFs for Gemma 4 12B and LFM2.5 8B A1B — DiffusionGemma offers speed over accuracy, while MoQ GGUFs enhance quantized model performance.
• Gemma 4 31B Quantization Comparison: Best FP8, NVFP4, and INT4 Models — Quantization effectively compresses Gemma 4 31B, reducing memory and improving speed with minimal accuracy loss.
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• Google's Gemma 4 AI models get 3x speed boost by predicting future tokens — Multi-Token Prediction (MTP) significantly accelerates local AI inference for Gemma 4 models via speculative decoding without quality loss.

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Kaitchup – AI on a Budget.