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.
• 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.
• You need llama.cpp from the last couple of weeks. Older builds fail with unknown model architecture: 'qwen35'. I hit this myself.
• 32GB of RAM comfortably runs Q4. 16GB runs Q2. The RAM table below has the numbers per quant.

...read more at terminalbytes.com