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DFlash 2 boosts Qwen 3.8 27B speed by 2.26x in llama.cpp benchmarks

By PulseAugur Editorial · [1 sources] ·

A user has benchmarked the new DFlash 2 speculative decoding method within llama.cpp, using the Qwen 3.8 27B model. The results show a 2.26x speed increase on real-world coding prompts without additional methods, and up to 4.68x when combined with an n-gram lookup table. The benchmark also highlighted that DFlash 2 requires less VRAM than its predecessor and that certain configuration parameters for speculative decoding did not perform as expected. AI

IMPACT This benchmark demonstrates significant speed improvements for local LLM inference, potentially enabling more complex tasks on consumer hardware.

RANK_REASON User benchmark of a new speculative decoding method for LLMs. [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 2 boosts Qwen 3.8 27B speed by 2.26x in llama.cpp benchmarks

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User benchmark of a new speculative decoding method for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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46 days old
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COVERAGE [1]

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

    I benchmark DFlash 2 (PR build) in llama.cpp on Qwen 3.8 27B against all speculative methods for 3 days. 2.26x on 100 real coding prompts, 4.68x with one n-gram drafter on top. Up to 8x on specific cases.

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vvncyh/i_benchmark_dflash_2_pr_build_in_llamacpp_on_qwen/"> <img alt="I benchmark DFlash 2 (PR build) in llama.cpp on Qwen 3.8 27B against all speculative methods for 3 days. 2.26x on 100 real coding prompts,…

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