For 10 days, Qwen3.8 27B ran locally on a Mac Studio as a background assistant, handling RSS digests and PDF filing. Its sudden popularity on r/LocalLLaMA prompted real-world testing. On the same machine, Qwen3.8 generates at ~14 tokens/s versus Qwen3.6's ~28.6 tokens/s, but uses about a third fewer tokens per answer, making wall-clock times similar.
The 1-bit quant (6.7GB) runs at 27 tokens/s but struggles to commit to answers. Newer llama.cpp builds are required; older ones fail with 'unknown model architecture: qwen35'. The model, a 27.3B parameter hybrid attention design with multimodal support and a 262,144-token context, performs well on benchmarks like SWE-bench Pro (61.7) and GPQA Diamond (89.2).
Community feedback highlights its practicality as a local alternative to paid APIs. Testing involved five timed runs per model, showing stable performance. On Apple silicon, inference uses the GPU, drawing 63.95W while the CPU idles at 6W, making it efficient for everyday tasks.
Source: Hacker News · Summarized by HeadlinesBriefing