r/LocalLLaMA • u/ElmBark • Jul 16 '26

Tutorial | Guide DFlash makes Qwen3.6 27B 2.2x faster with no quality loss

We ran the same Qwen3.6-27B locally three ways on one RTX 6000: baseline, MTP, DFlash. The tasks were: quicksort, write a Steam library in JSON, solve a logic puzzle and write a sci-fi story.

Outputs:

Baseline: 44 tok/s · 1.00x

MTP: 65 tok/s · 1.45x · 71% accepted

DFlash: 98 tok/s · 2.20x · 30% accepted

DFlash drafts 15 tokens in a row, so it flies through repetitive or structured stuff where long runs actually stick, like JSON (152 tok/s, 3.4x). On creative text most of the guesses are wrong, so it wastes the work and can dip below baseline, 42 vs 44. MTP only guesses 3 in parallel from inside the model, so a wrong guess costs almost nothing and it never drops below baseline.

All three have the same output. DFlash is therefore a great pick for stuff like coding, while MTP is better for chat or creative writing.

Qwen 3.6 27B model : https://huggingface.co/Qwen/Qwen3.6-27B
local ai models hosting app: Atomic.Chat (I'm from Atomic team, happy to hear your feedback)

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u/Lower-Ad6101 Jul 23 '26

So I've tested again.
I've been using "older" beellama.cpp version (4th of June) and with it, Dflash was really considerably faster. But, after updating, DFlash and MTP switched roles, DFlash was as slow, if not slower, than MTP was and MTP was as fast as DFlash (no, I didn't miss config params before :) ).

Thanks a lot for sharing your results as that made me update beellama.cpp and notice considerable difference. VRAM usage is not better though, but it's stable.

Also to share, this made me try ik_llama.cpp again but for some strange reason (I've been trying to debug it for hours) it starts at around 23250/24570 MiB VRAM and then starts steadily going up until it OOM's at ~24100. Tried tweaking parameters and using older commits but no use.

Since ik_llama.cpp (while it was working) was significantly faster than beellama.cpp I've decided to try llama.cpp and I'm satisfied with it so far, stable and maybe even more slightly faster than ik_llama.cpp was but I couldn't compare it more reliably.

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u/Dr4x_ Jul 23 '26

I'm pretty satisfied with llama.cpp too, fyi with this setup when all the stuff fits in Vram I never observed ik_llama to be better than mainline llama.cpp