Packed twin inference doubles Qwen3.6-27B throughput on one MI50
Original: 2X tk/s (from 19.4 -> 38.1 tk/s on 1 x MI50) Playing with a hypothesis like speculative decoding.. but instead of an additional side model, exploiting that I can run multiple computations side-by-side AS IF I had Qwen3.6-27B loaded twice in memory - small quants don't use all the available compute.
An early LocalLLaMA experiment reports 19.4 to 38.1 tk/s for Qwen3.6-27B on one MI50.
A LocalLLaMA user shared an early packed-twin-inference experiment for local LLM acceleration. The idea resembles speculative decoding, but uses the same quantized model side-by-side instead of a smaller draft model. On a single AMD MI50, the author reports Qwen3.6-27B improving from 19.4 to 38.1 tk/s, with Q8-or-lower quantization as the main target.
這篇 r/LocalLLaMA 貼文是作者 bigattichouse 對一個本地 LLM 推論加速實驗的早期分享，並附上 GitHub 專案 packed-twin-inference。作者表示，目前內容還不是可直接被廣泛採用的 llama.cpp patch，之後若整理成可用形式會再發完整文章；現階段主要是因為實驗結果令人興奮，所以先公開概念與數據。
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