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Qwen 3.8 Flash model quantization achieves near-original performance

A new quantization method for the Qwen 3.8 Flash model, developed by Unsloth, reportedly achieves 99% of the performance of the original BF16 version. This new quantization, referred to as UD v3.1, is claimed to be 15% better than the previous GSQ RCO quantization method while maintaining a similar file size. The findings are based on an average of 12 benchmarks and approximately 30 billion tokens of generated data, offering reassurance to users running the model locally. AI

IMPACT New quantization techniques for open-source models can improve local inference performance and accessibility.

RANK_REASON The item discusses a new quantization method for an existing open-source model, detailing its performance relative to the original. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Qwen 3.8 Flash model quantization achieves near-original performance

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9 / 100
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The item discusses a new quantization method for an existing open-source model, detailing its performance relative to the original. [lever_c_demoted from research: ic=1 ai=1.0]
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model release
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High
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Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/DigiDecode_ ·

    Qwen 3.8 Flash Next GSQ RCO quant as good as BF16

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xnutr6/qwen_38_flash_next_gsq_rco_quant_as_good_as_bf16/"> <img alt="Qwen 3.8 Flash Next GSQ RCO quant as good as BF16" src="https://preview.redd.it/ql6whxx4umzh1.jpeg?width=640&amp;crop=smart&amp;auto=webp&a…