Gemma 4 26B-A4B GGUF Benchmarks

Hey r/LocalLLaMA we conducted KL Divergence benchmarks for Gemma 4 26B-A4B GGUFs across providers to help you pick the best quant.

• Mean KL Divergence puts nearly all Unsloth GGUFs on the Pareto frontier
• KLD shows how well a quantized model matches the original BF16 output distribution, indicating retained accuracy.
• This makes Unsloth the top-performing in 21 of 22 sizes. Similar trend for 99.9% KLD and others.
• We also updated our Q6_K quants to be more dynamic. Previously, they were optimized, just now they're a bit better - no need to re-download though - it's up to you if you want a slightly better version. The previous quant was perfectly fine but this one is slightly bigger. The same was done for Qwen3.6.
• We're also introducing a new UD-IQ4_NL_XL quant that fits in 16GB VRAM. UD-IQ4_NL_XL (14.6GB) sits between UD-IQ4_XS (13.4GB) and UD-Q4_K_S (16.4GB). The same was done for Qwen3.6.

For HQ versions of the graphs as Reddit mobile compresses it. See: Gemma 4 Benchmarks and Qwen3.6 Benchmarks

We also updated our MLX quants to be more dynamic with better layering selection (there are limitations due to MLX): See here

Gemma 4 GGUFs: https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF

Qwen3.6 GGUFs: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF