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

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 →

• DFlash 2
• llama.cpp
• Qwen 3.8 27B

• infra
• product

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