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Qwen 3.6 27B FP16 vs Q8 quantization performance debated

By PulseAugur Editorial · [1 sources] ·

A user on Reddit's r/LocalLLaMA subreddit is inquiring about the performance differences between FP16 and Q8 quantization for the Qwen 3.6 27B model. They are experiencing slow FP16 performance on their setup and are seeking to understand if there are notable differences in weights and cache. Additionally, the user is asking about expected tokens per second (TPS) for this model on a Strix Halo system during coding tasks. AI

IMPACT Discussion on model quantization and performance impacts user experience and hardware optimization.

RANK_REASON User discussion about model quantization and performance. [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 3.6 27B FP16 vs Q8 quantization performance debated

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User discussion about model quantization and performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 Deutsch(DE) · /u/Forward_Jackfruit813 ·

    FP16 on Qwen 3.6 27B

    <!-- SC_OFF --><div class="md"><p>Have there been any notable difference between Q8 and FP16 on both the weights and the cache? I know the jump to Q8 is significant. I would test myself, but FP16 on my setup is painfully slow.</p> <p>Also side question, is ~14TPS around the numbe…

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