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Run Qwen3.8 27B locally: real numbers from my Mac Studio
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.
Qwen3.8-27B is a 27.3B parameter dense model with a hybrid attention design (the architecture tag in the GGUF is qwen35, which matters later). It’s multimodal, with image and video understanding built in, carries a 262,144-token native context window, and ships under Apache 2.0. The official model card claims 61.7 on SWE-bench Pro and 89.2 on GPQA Diamond, numbers that would have been frontier-lab territory a year ago.
The community reaction skipped right past that benchmark table. What lit the threads up was what people did with the model in its first week: one team wired it into their coding pipeline as a drop-in for a paid API model and reported it held up, and OCR testers claimed quality above some commercial cloud tiers. The line from the most-upvoted thread that stuck with me: “this is the first local model that feels like more than a toy.”
My contribution is the one measurement most of those charts are missing: what this model actually does on Apple silicon you can buy today.
My daily machine is a Mac Studio M3 Ultra with 256GB of unified memory, the same box I used for the DeepSeek V4 Flash guide. I ran five timed generations per model through ollama run --verbose, varied technical prompts, ~200-500 word answers, and averaged the stats. Both models are the default Ollama Q4_K_M quant, both almost exactly 17GB on disk.
The headline number first: the new model generates at half the speed of its predecessor. Same parameter count, same quant size, same machine. The hybrid attention architecture is new, and the Metal kernels in Ollama clearly haven’t caught up yet. I expect this gap to narrow as the runtimes mature; the same thing happened with other novel architectures.
It didn’t actually cost me time, though. Qwen3.8 answered the same prompts in roughly 1,000 tokens where 3.6 rambled through 2,000-3,300. The arithmetic: 2,058 tokens at 28.6 tok/s is 72 seconds, 955 tokens at 14.2 tok/s is 67 seconds. Slower per token, faster per answer.
While it generates, the CPU barely notices, because on Apple silicon the inference runs on the GPU through Metal. The cover image of this post is exactly that moment, captured with macmon mid-generation: GPU pinned at 100% pulling 63.95W, CPU sipping 6W, answer streaming the whole time.