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LocalLLaMA@sh.itjust.worksEnglish · 23 days ago

Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses

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Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses

inari@piefed.zip to

LocalLLaMA@sh.itjust.worksEnglish · 23 days ago

11

Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses - Quesma Blog

I test Unsloth GGUFs of Qwen3.8 27B (Q4_K_M, UD-Q2_K_XL, UD-IQ1_S) with llama.cpp on GPQA Diamond, IFBench, Terminal-Bench 2.1. Q4_K_M matches BF16 abd fits an RTX 4090.

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Shimitar@downonthestreet.eu
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14 days ago
Great model. The best so far for my usage (agents and coding). I can get 15t/s on my dual rtx a4000 setup. I use the Q5, with 300k context.

LocalLLaMA@sh.itjust.works

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Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.

Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.

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Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they’re still using the same algorithms since <over 10 years ago>.

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