Qwen3.6 27B speeds split local users

The useful signal here is not that one rig is “slow,” but that Qwen3.6-27B makes inference-stack choices brutally visible: context length, quant, KV cache, split mode, and serving engine all matter.

• –Qwen3.6-27B is a 27B open-weight model with vision support and a native 262K context window, so 128K context plus Q8 cache is a heavy local-serving configuration.
• –llama.cpp users are reporting materially different speeds depending on GPU mix, quantization, flash attention, split strategy, and whether the model stays fully in VRAM.
• –vLLM and MTP-backed runs appear much faster in the thread, reinforcing Qwen’s own guidance that production throughput favors engines like vLLM, SGLang, or KTransformers.
• –For developers, the practical takeaway is to benchmark at the actual context length and modality settings they plan to use, not just compare headline tokens/sec.