Ran Qwen 3.5 9B on M1 Pro (16GB) as an actual agent, not just a chat demo. Honest results.

Quick context: I run a personal automation system built on Claude Code. It's model-agnostic, so switching to Ollama was a one-line config change, nothing else needed to change. I pointed it at Qwen 3.5 9B and ran real tasks from my actual queue. Hardware: M1 Pro MacBook, 16 GB unified memory. Not a Mac Studio, just a regular laptop. Setup: brew install ollama ollama pull qwen3.5:9b ollama run qwen3.5:9b Ollama exposes an OpenAI-compatible API at localhost:11434. Anything targeting the OpenAI format just points there. No code changes. **What actually happened:** **Memory recall**: worked well. My agent reads structured memory files and surfaces relevant context. Qwen handled this correctly. For "read this file, find the relevant part, report it" type tasks, 9B is genuinely fine. **Tool calling**: reasonable on straightforward requests. It invoked the right tools most of the time on simple agentic tasks. This matters more than text quality when you're running automation. **Creative and complex reasoning**: noticeable gap. Not a surprise. The point isn't comparing it to Opus. It's whether it can handle a real subset of agent work without touching a cloud API. It can. The slowness was within acceptable range. Aware of it, not punished by it. Bonus: iPhone Ran Qwen 0.8B and 2B on iPhone 17 Pro via PocketPal AI (free, open source, on the App Store). Download the model once over Wi-Fi, then enable airplane mode. It still responds. Nothing left the device. The tiny models have obvious limits. But the fact that this is even possible on hardware you already own in 2026 feels like a threshold has been crossed. The actual framing: This isn't "local AI competes with Claude." It's "not every agent task needs a frontier model." A lot of what agent systems do is genuinely simple: read a file, format output, summarize a short note, route a request. That runs locally without paying per token or sending anything anywhere. The privacy angle is also real if you're building on personal data. I'm curious what hardware others are running 9B models on, and whether anyone has integrated them into actual agent pipelines vs. just using them for chat. Full write-up with more detail on the specific tasks and the cost routing angle: https://thoughts.jock.pl/p/local-llm-macbook-iphone-qwen-experiment