Testing 16 Quantization Schemes for Qwen 27B: GGUF Offers Best Quality-Size Tradeoff
Hefty_Wolverine_553 · reddit · 2026-08-11
A developer conducted an in-depth comparison of 16 quantization schemes for the Qwen3.6 27B model, including GGUF, NVFP4, and AWQ, to determine their impact on the model's output probability distribution.
Methodology
Using a dataset of 182,000 tokens from agentic tool-use conversations, the benchmark calculates the Kullback-Leibler divergence (KLD) between the quantized model and an unquantized reference. Lower KLD indicates higher fidelity.
Key Findings
- GGUF Performs Best Overall: Weight-only GGUF formats maintain the lowest KLD at almost every given size, primarily because they do not quantize activations at all.
- High Variance in vLLM Quants: Schemes quantizing both weights and activations (like NVFP4 W4A4) show significantly more quality loss than smaller GGUF models. AWQ and NVIDIA's mixed NVFP4 tie in performance.
- Practical Takeaways: Quantization format alone isn't enough to predict quality; the specific recipe matters. While activation quantization improves throughput, it inevitably sacrifices model quality.
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