Tip: BeeLlama.cpp Fork Enables Qwen 3.6 27B Q5 at 200K Context on a Single RTX 3090 via DFlash + TurboQuant

r/LocalLLaMA·medium signal

A llama.cpp fork called BeeLlama.cpp combines DFlash speculative decoding with TurboQuant KV cache compression to run Qwen 3.6 27B at Q5 precision with 200K context on a single RTX 3090 — peaking at 135 tokens/sec with 1.5–2x speedup from multi-token prediction. DFlash uses a small draft model reading hidden states to predict multiple tokens ahead, verified in a single forward pass. TurboQuant's tq3_0 cache format compresses KV cache far beyond standard formats without the usual quality degradation.