DFlash makes Qwen3.6 27B 2.2x faster with no quality loss
Summary
DFlash is a method that accelerates Qwen3.6 27B model inference by 2.2x without quality degradation.
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@zhijianliu_: DFlash for Qwen3.6-35B-A3B just dropped The community was running the day-1 preview before we even finished training. N…
Z-lab releases DFlash for Qwen3.6-35B-A3B, a model fine-tuning/compression technique, with training complete and weights now available on GitHub and HuggingFace.
z-lab/Qwen3.6-35B-A3B-DFlash
z-lab releases DFlash, a speculative decoding drafter that uses a lightweight block-diffusion model to draft 15–16 tokens in parallel, yielding up to 2.9× speedup for Qwen3.6-35B-A3B inference.
z-lab/Qwen3.6-27B-DFlash
This article introduces Qwen3.6-27B-DFlash, a specialized drafter model for DFlash, a novel speculative decoding method using block diffusion to accelerate inference speed. It provides installation instructions for vLLM and SGLang to enable parallel drafting with the target Qwen3.6-27B model.
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.
A user benchmarks the newly merged DFlash speculative decoding method in llama.cpp on Qwen 3.6 27B, achieving up to 4.44x speedup at 36K context compared to baseline, with detailed leaderboard and quality tests.
2.5x faster Qwen3.6 NVFP4 Unsloth quants
Unsloth releases quantized Qwen3.6 models using NVFP4 format, achieving 2.5x faster inference speeds.