Model Releases
DFlash2 speeds Qwen 3.8 27B up to 4 times
llama.cpp pr #27342 adds dflash2, so i rented an rtx 6000 and ran the same four prompts through four decoding setups on qwen3.8 27B median results over the four tasks: baseline 47.4 tok/s mtp 114.7 to
llama.cpp pr #27342 adds dflash2, so i rented an rtx 6000 and ran the same four prompts through four decoding setups on qwen3.8 27B median results over the four tasks: baseline 47.4 tok/s mtp 114.7 tok/s dflash 99.3 tok/s dflash2 140.6. tok/s so on average 3x for dflash2 though i have to point out that it's far from a 3x gain some of the time, on one of the test it struggled to achieve a 1.5x gain, it really just depends on the task you give to the model the races are sped up in some places, so that the video lasts roughly 30 seconds, but the tok/s and acceptance % on screen are the real i'm from the atomic.chat team - we publish our own quants on hf and make a desktop and mobile app for running local models. so any feedback welcome - we're building this for you folks about dflash2: https://inco.ai/blog/dflash2/ submitted by /u/Top-Eye-8104 [link] [comments]
Related
- After pushing 1M+ tokens through Qwen 3.8 27B, here is my optimal llama.cpp config for 16GB VRAM (73k Context, Agentic Coding)
- DFlash 2 available for Qwen 3.8 27B and Muse Glimmer
- BeeLlama.cpp: advanced DFlash & TurboQuant with support of reasoning and vision. Qwen 3.6 27B Q5 with 200k context on 3090, 2-3x faster than baseline (peak 135 tps!)
Source: r/LocalLLaMA | 2026-08-19