GoKawiilQwen3.8 27B benchmarked on Mac Studio M3 Ultra: half the speed, third the tokens
A user ran Alibaba's new Qwen3.8 27B model locally on a Mac Studio M3 Ultra for 10 days, using it for daily tasks like summarizing RSS feeds and renaming scanned PDFs, then formally benchmarked it against its predecessor. The Q4_K_M version generated at about 14 tokens per second versus 28.6 tokens/s for Qwen3.6 27B, but produced answers using roughly a third as many tokens, making total response time nearly equal. A 1-bit quantized version ran much faster at 27 tokens/s and stayed factually accurate but struggled to commit to definitive answers.
GoKawiil's interpretation of the reporting above, not reported fact.
This kind of independent, hands-on benchmarking helps the growing local-LLM community understand real-world tradeoffs beyond marketing claims, especially around hardware requirements and quantization effects. It also flags a practical compatibility issue—older llama.cpp builds fail on the new architecture—that could trip up other users adopting the model.
- Qwen3.8 27B is slower per token than Qwen3.6 but far more token-efficient, roughly equalizing real-world response times.
- Running the model requires a recent llama.cpp build due to its new 'qwen35' architecture tag; older versions throw errors.
- 32GB RAM comfortably handles Q4 quantization, while 16GB is enough for Q2, per the tester's benchmark table.
Apple Mac Studio (M3 Ultra) — This is the exact class of machine the article benchmarks on: a Mac Studio with M3 Ultra and its large unified memory pool is what makes running a 27B local model at usable speeds possible. If you want to keep a local assistant summarizing feeds and filing PDFs all day without touching the cloud, this is the box to do it on.
See Apple Mac Studio (M3 Ultra) on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: terminalbytes.com, 2026-08-28
Published there as: “Run Qwen3.8 27B locally: real numbers from my Mac Studio”
Read the original report → The summary and analysis above are GoKawiil's own, written from reporting by the source above. Facts and quotes belong to the original publisher.