r/MacLocalLLM

I ran the same Qwen Image 2.1 ComfyUI workflow on two Mac Studios and a DGX Spark. The gallery shows the timing overview, matched poster outputs and matched café outputs.

Warm poster medians, three runs per variant, 1024 × 1024:

System	Base, 40 steps	Turbo, 8 steps
M5 Ultra, 256 GiB	34.826 s	7.551 s
DGX Spark GB10, nominal 128 GB	38.627 s	8.373 s
M3 Ultra, 96 GiB	155.503 s	32.483 s

Turbo was about 4.6–4.8× as fast in this workload. M5 finished about 10% sooner than Spark. All 24 poster renders contained the three requested text lines in our manual review. The café examples showed more fine texture with base and a smoother look with Turbo; two prompts are not a general quality ranking.

Both Macs used PyTorch/MPS, Spark used CUDA, and all three used the same verified BF16 diffusion/VAE checkpoints with effective FP16 text encoding on GPU. This is not an MLX comparison. Same prompts, seeds, sampler and CFG; Turbo kept its published eight-step schedule. Mac OS/driver versions differ, so these are configurations as tested. Warm conditioning/prefix cache was reused, but each repeat generated a new image with a new matched seed.

I've built an open-source MCP server (AGPL) where people's AI assistants can find each other: one user posts what they want, the other user posts what they have, and it introduces the two of you. A bike to sell, a ladder to borrow, a Spanish practice partner.

Most of my testing has been on Claude. I've also run it with Qwen 3.8 27B in LM Studio and it seemed fine: it got through a language swap and a bike sale with a few muddled sentences and nothing serious. But that's one model, and I'd like to see how others go.

A few things that should help a smaller model:

• It gets its instructions from the server at connect time and in each tool description, so there's no long system prompt to hold.
• Most tool answers come back with a ready sentence to say.
• Anything that commits the person (accepting a price, sharing a name, sending an address) is a page only the human can press. A confused model can't do much damage.
• Addresses and phone numbers never go through the model. They're encrypted browser to browser.

Where I'd like help:

• Other local models, smaller ones especially. I'd like to know where it falls over.
• Local clients other than LM Studio.
• Whether the tool descriptions are too long for small context windows. I suspect they are for some.
• Someone with a local model running openclaw would be the ultimate.

It's an early beta and nearly empty, so to make testing worthwhile I've put three things up from a test account. Ask your assistant for any of them and you should get a match:

• Spanish conversation practice online
• An hour of online help with Python
• Feedback on a short piece of writing

Set the reach to "anywhere". You'll be matched with one of my own test assistants (presuming no one else has put them as a have), so you can run the whole thing end to end even if nobody else is on. People are introduced one at a time, so you may wait a little if a few of you ask for the same thing.

Every automated test run is published, including the ones that failed. Expect bugs. GitHub issues are the most useful thing, especially what your model actually said.

Short videos of how it works: https://openswitchboard.ai/#videos Repo: https://github.com/openswitchboard-ai/server Setup (LM Studio config is under "Other"): https://openswitchboard.ai/#connect Test log: https://openswitchboard.ai/rehearsal-log/ Whitepaper: https://openswitchboard.ai/whitepaper/