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Qwen3.8-Flash-Next model released with GSQ-RCO quantization, reducing size and boosting speed

By PulseAugur Editorial · [2 sources] ·

A new version of the Qwen3.8-Flash-Next model, named GSQ-RCO, has been released, offering significantly reduced file sizes while maintaining near-baseline quality. This quantization method cuts the model size from approximately 80-95GB down to 68-76GB. A specific variant, Q2_0, is highlighted for its speed, claiming up to 6.2x better prompt throughput in coding tasks and improved overall latency compared to other quantization formats. AI

IMPACT This release offers a more efficient version of Qwen3.8-Flash-Next, potentially enabling wider adoption on resource-constrained devices and improving inference speeds for specific tasks.

RANK_REASON Release of a new model quantization with performance benchmarks.

Read on Hugging Face Trending Models →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Qwen3.8-Flash-Next model released with GSQ-RCO quantization, reducing size and boosting speed

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Research
Release of a new model quantization with performance benchmarks.
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2 independent sources
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model release, infra
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High
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57 days old
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COVERAGE [2]

  1. Hugging Face Trending Models TIER_1 Deutsch(DE) · ISTA-DASLab ·

    ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF

    image-text-to-text · 13,471 downloads · 97 likes

  2. r/LocalLLaMA TIER_1 English(EN) · /u/BullfrogScary8947 ·

    [Release] SOTA GGUFs for Qwen3.8-Flash-Next: GSQ-RCO Providing Near Baseline Performance

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1whu67w/release_sota_ggufs_for_qwen38flashnext_gsqrco/"> <img alt="[Release] SOTA GGUFs for Qwen3.8-Flash-Next: GSQ-RCO Providing Near Baseline Performance" src="https://external-preview.redd.it/7Dno1MpvxCV47U…

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