Updated Qwen3.5-9B Quantization Comparison

A new community-driven analysis provides a crucial benchmark for developers running Alibaba's Qwen3.5-9B language model locally. The study evaluates over 30 different quantized versions of the model—files that reduce its size for efficient deployment on consumer hardware—using KL Divergence (KLD) as the primary metric. KLD measures how much a quantized model's probability distribution drifts from the original BF16 baseline, with lower scores indicating higher faithfulness. This data-driven approach moves beyond noisy metrics like perplexity (PPL) to give users a reliable basis for choosing the right file for their needs.

The analysis reveals that the `eaddario/Qwen3.5-9B-Q8_0` quantization currently leads the pack with a KLD score of 0.001198 and a file size of 8.87GB, making it the most faithful to the original model. Other top performers include quantizations from `unsloth` and `bartowski`. The rankings span a wide range of sizes, from the 12GB `Q8_K_XL` variants down to sub-5GB `IQ4` versions, allowing users to make informed trade-offs between model fidelity and storage or memory constraints. This work demystifies the often confusing landscape of community quantizations.

Ultimately, this benchmark empowers developers and researchers to deploy the 9-billion-parameter Qwen model more effectively. By providing clear, comparative data, it reduces the guesswork in selecting a quantization, ensuring users get the best possible performance from their chosen model size, whether for prototyping, research, or production applications on local machines.