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DFlash boosts Qwen 3.6 27B performance in llama.cpp by 4.44x

By PulseAugur Editorial · [1 sources] ·

A user on Reddit's r/LocalLLaMA forum has shared performance benchmarks for the Qwen 3.6 27B model when using the newly merged DFlash feature in llama.cpp. The tests, conducted on an RTX 6000 PRO, showed a significant speed increase of 4.44x at a 36K context length compared to previous methods. The DFlash feature, which utilizes speculative decoding with a block diffusion drafter, fills blocks of tokens in a single pass, enhancing efficiency. AI

IMPACT Demonstrates potential for significant speedups in local LLM inference, enabling faster processing at larger context windows.

RANK_REASON User-conducted benchmark of a new feature integrated into open-source software. [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 →

DFlash boosts Qwen 3.6 27B performance in llama.cpp by 4.44x

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User-conducted benchmark of a new feature integrated into open-source software. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/FantasticNature7590 ·

    I tested freshly merged DFlash in llama.cpp on Qwen 3.6 27B Local AI win. 4.44x faster at 36K context. Here are my findings RTX 6000 PRO.

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1uq0h4o/i_tested_freshly_merged_dflash_in_llamacpp_on/"> <img alt="I tested freshly merged DFlash in llama.cpp on Qwen 3.6 27B Local AI win. 4.44x faster at 36K context. Here are my findings RTX 6000 PRO." src…

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