GoKawiilWhat it actually does for me all day
How to run it (and the gotcha that cost me 20 minutes)
My numbers: 3.8 vs 3.6 on the same machine
For the past 10 days, Qwen3.8 27B has been quietly running on my Mac Studio as a background assistant. It summarizes my RSS feeds into a morning digest, renames and files the PDFs I scan into something searchable, and handles whatever summarizing chore I throw at it. Mundane stuff. That’s the appeal: this is the first local model I’ve trusted enough to leave alone with mundane stuff.
Then last week the model was suddenly everywhere on r/LocalLLaMA, my feeds filled up with benchmark charts, and I realized I’d been sitting on the one thing most of those threads were missing: a machine that can actually run it properly, and time to measure it.
So I benchmarked it. Five timed runs per model, same prompts, same machine, plus a 1-bit experiment that surprised me twice. Here’s everything I measured, and what it means for the hardware you’d need to run this thing yourself.
TL;DR Qwen3.8 27B (Q4_K_M, 17GB) generates at ~14 tokens/s on my Mac Studio M3 Ultra via Ollama. Its predecessor qwen3.6:27b does ~28.6 tokens/s on the same machine.
on my Mac Studio M3 Ultra via Ollama. Its predecessor qwen3.6:27b does on the same machine. It also answers the same prompts in roughly a third the tokens, so wall-clock per finished answer is close to a tie.
The 1-bit quant (6.7GB) runs at 27 tokens/s in llama.cpp and gets facts right, but it cannot commit to an answer.
in llama.cpp and gets facts right, but it cannot commit to an answer. You need llama.cpp from the last couple of weeks. Older builds fail with unknown model architecture: 'qwen35' . I hit this myself.
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