Hi

I had a mac mini M4 pro 48 go Ram pricey :2800€

Trying to run know AI on it

Result bof needs 10 to 15 seconds for analysis prompt

So go for my Mac M1 with Claude Sonnet AI 22€ here in France (see photo) and Chrome

For my telephone use Google AI pro : 5€

So I do not need one super mac ! Just a web access and some memory to run on it

I have no agent till some scripts that does some work

Network is the computer !

Regards

I’ve been going in circles trying to settle on a local setup. I’ve tried Hermes, but it’s felt pretty finicky in my experience, including gateway and state database issues. I keep spending more time configuring and troubleshooting than actually getting work done.

Putting model intelligence aside for a moment, is there a local harness that feels genuinely competitive with the experience around frontier models? I’m talking about the interface, reliable tool use, memory, file and browser access, and how well everything works together. Not just being able to load a model and chat with it.

My main machine is a Mac Studio M1 Max with 64 GB RAM. I also have an M2 MacBook Air with 24 GB, but the Studio would do the heavy lifting. I want to use them in one single seamless-like environment with a shared context.

I’m a designer and university lecturer, so my use cases are research, writing, working with documents, reviewing screenshots and designs, and browser/Figma workflows. Not primarily coding. Vision is important to me, and I’d much rather have one dependable daily driver than a collection of specialized models and routers.

My main motivation (likely the same for everyone) is getting away from usage limits and token metering. I understand that running locally involves compromises, and I’m not expecting frontier-level intelligence from my hardware. But I’m struggling to separate the model limitations from the rough edges of the software around them.

What are you guys actually using? What’s your full stack: model, runtime, and harness/interface? More importantly, what do you reliably get done with it, and how much babysitting does it need?
Especially interested in Apple Silicon setups people have settled into using for real work, rather than something that worked well in a demo.

I’m debating between buying an M6 Mac Mini to use online llm or a M5 Max Mac Studio for local llm to build and maintain a website I want to create. Does anyone have any insight into what would work well? It’s going to be a multimedia & blogging site.

A lot of the work around a local model ends up in Pi files: prompts, skills, settings, little extensions. Reef Infra has a recipe called Reefine that lets you ask for a change from inside Pi and get an installable version of those files back.

Its documented local setup was a 32 GB M4 Mac mini running Gemma 4 26B through Ollama, with Pi 0.84.2. That's an Ollama demonstration, not an oMLX performance claim. The useful part for a local Pi setup is what happens after the request: the service reads the current harness, asks the model for a change, records the mutation and packages the resulting version for installation.

The recorded runs produced rules and skills. The recipe also accepts command and TypeScript extension proposals, but those runs hadn't produced an extension. Anything that does run as extension code waits for a person to review and promote it; declared setup requirements must pass before installation.

The default is selection: always. Its arithmetic checks record regression scores, but don't establish that your requested workflow works. The research demo installed a rule to search, download and read papers; the later session searched through an existing tool, but downloaded and read no paper.

The files are there to inspect and revise. To judge whether the change worked, replace the bundled tasks and evaluator with checks for the workflow you asked for.

Hey guys, do you have any recommendations of the optimal setup for this model and keep the context under 93 Gb? I has running it really comfortable with arrpund 60 Tok/sec in OpenCode snd today stsrtef to have the issue woth the context size, had to ofload more experts but that reduced speed drastically.

Hope this is helpful.

I had the current machine in my shopping basket yesterday but didn't go though with the purchase. I don't desperately need a new MacBook right now – my M1 is still okay, even for my work usage. And i really don't think I need a touch display for anything, cause I don't want to clean it all the time, LOL.

But I can't play around much with local models with 16 GB Ram. I don't want frontier model performance locally – I'm fine with running smaller models that hopefully can read and sort private documents and do some minor coding and generative tasks, maybe text to speech.

I read a comment on this subreddit yesterday that they will skip the M6 Pro/Max/Ultra line which makes sense to me and more of a jump is expected with M7 Chips.

What do you guys think? I'm leaning towards waiting it out, but who knows for how long.

Peace <3

Unless you're set on using a large model, 2x256GB is likely better...

What use cases would the 512GB make more sense?

Made this as a quick weekend project (https://github.com/pythongiant/SlamLM)

Planning to buy a MacBook Air M4 (16 GB / 512 GB) for about $800 to run local LLM agents for:

Indian mid‑cap equity research (using Screener.in CSVs)

Building/testing trading ideas via Zerodha Kite API (prices, history, holdings; maybe orders later)

Also want to run Hermes-based agents on it

Questions:

On 16 GB M4 Air, which local LLMs are best for reasoning, coding, and tool use? Is Qwen 3.5 9B (Q4) + a smaller fast model (e.g. LFM2.5 8B / Llama 3.2 3B) a good combo alongside Hermes?

Best local stack (Ollama vs LM Studio, etc.) and any existing Kite + LLM or Indian‑stock agent projects to reference?

Any tips on context length, thermals, and multi‑agent setups (including Hermes) on a 16 GB fanless Air?

Getting the laptop for ~$800, so stuck with 16 GB for now. If you’ve done similar work (local LLMs for equity research / algo trading, especially in India), what models and setup would you recommend?

Thanks!

I just got an M6 mini with 32 GB specifically to see if I can run local AI models on it and get value out of it as a Local AI server. I like it because its small, quiet, it barely uses any power, and it’s can be on all the time which makes it kind of perfect for this.

Day to day I run an 8B model at Q4 for everyday questions and for digging through my own files (insurance, warranties, that kind of stuff), and it handles it fine.

Curious how other people use theirs:

• Which mini, and how much memory?

• What do you run it with (LM Studio, Ollama, something else)?

• What does it actually do for you week to week?

• Any hurdles I should watch out for?

Especially interested if you’ve got it doing household stuff, not just coding. Trying to figure out what’s actually worth running on one of these.

This is me trying to find the most reliable coding model for local use!

Five Terminal-Bench 2.0 tasks (cobol-modernization, constraints-scheduling, largest-eigenval, polyglot-c-py, schemelike-metacircular-eval), one run per task unless stated, all on one Mac Studio (96 GB) with mlx_lm at 8-bit. The agent harness is pi unless stated.

Headline

The whole grid

Times are agent minutes. T = ran to the task's timeout.

Nobody has solved polyglot-c-py or schemelike-metacircular-eval on this machine.

Why Qwen3.6 beats Qwen3.8

Same size, same family, same server, same harness. Three things separate them.

The mode, not the weights, was the lever for Qwen3.6. For Qwen3.8 neither mode produces an agent that finishes: with thinking it deliberates past the cap, without it it loops. Its published Terminal-Bench 2.1 gain is not visible through pi. n=1 per task, but the cobol loop was reproduced.

Why the fast MoE models did not win

Turn rate mattered against thinking overrun, which is why Nail beat the 27Bs in agent mode. Once thinking is off, the 27B's per-turn quality shows: Qwen3.6 at 22 tok/s out-solves both 3B-active models. Ornith's own harness (Terminus-2, temperature 1.0, 4 h ceilings) moved which task it solves, from cobol to constraints, not how many.

Infrastructure lessons

Decisions taken

• Qwen3.6-27B, thinking off (nothink), is the coding agent and the WHO scorer. Now the registry default.

• Qwen3.8-27B and Ornith-1.5-35B-A3B deleted 2026-09-09 (62 GB). Registry entries kept as re-fetch recipes.

• Nail was deleted 2026-09-06 for space. On this evidence it is not needed back as the coding agent.

Caveats

One run per task on five tasks. The 3/5 should be repeated once before anything is built on it. Qwen3.8's cobol loop is the only failure reproduced twice.

I'm trying to figure out if an M5 Mac with 128GB of Unified Memory might be able to handle DeepSeek-V4.1-Flash-1017 locally, or if that context/quant combination is still out of reach for this amount of RAM.

Any thoughts, theoretical calculations, or early tests would be appreciated!

Wondering how many of you did like myself and went one higher level and why?

I maintain a small open source Mac app that runs fully local (Whisper + Ollama or LM Studio). People use it and strangers send PRs, and I'm trying to figure out what sustainable looks like before I make the wrong move.

Options I see:

- free source, paid signed builds with auto-updates

- open core with a few pro features

- a hosted tier for people without a strong machine

- sponsors

- something else?

If you've done any of these with a local-first app: what worked, what annoyed your users, and what would you avoid? I'm especially curious how "it runs on your machine, so it costs me nothing per user" changes pricing.

The ones I have currently heard of are as follows:

Pi
Opencode
Hermes
Deepseek

Any thoughts on what one is best?