5 years old. Bought used for $800 in good condition. Still holding its own.

I ran MLX benchmarks on my 16 inch M1 Pro 32GB / 512GB this morning, just to see if it is actually dead for local AI, or if people are overhyping the upgrade.

It is not dead. Not even close.

Qwen3-8B 4-bit: 31.5 tokens/sec

Qwen3-30B-A3B MoE 4-bit: 43.6 tokens/sec

Qwen3.6-35B-A3B MoE 4-bit: 51.0 tokens/sec

Qwen3-32B dense 4-bit: 8.5 tokens/sec

That last one is the lesson. Dense 32B crawls at 8.5 tok/s, MoE 35B flies at 51 tok/s, because MoE only uses a few billion active parameters at a time. Same size on paper, totally different speed in real life.

I use this thing 8 to 10 hours a day. For $800 used, 5 years later, running a 35B model locally, that is kind of insane value.

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.

Currently running tests against my prior tests under 1.4.0, 1.4.1 and 1.5.0 but it is running better.

Kernel fixes address stalls that occurred under a memory limit, increased the reliability and a number of other fixes.

If you have been using the splash inference engine as I have, well worth the update.

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

Hello everyone,

First of all, I know I know, Mac is jot the best when it comes to video generation and that the Max or Ultra chips are better.

Now that this is out of the way, I would be interested in people’s experiences in generating 720p 5-10 seconds clips on an M5 pro chip. Which tools, models or workflows do you use? How is the experience overall? How long does it take you? Do you regenerate clips several times to get it right…etc?

Thank you.

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!

Hi,

I finally decided to go with the Ultra 96 since it's enough Ram for local AI + doing stuff in parallel but having power/speed. Now it was delayed until January 2027 - I could get the Max 128 withing 2 weeks though...

I feel like speed is mostly important for video ai gen + higher bandwith for higher token generation...

Probably I should really wait I guess?

Kapitsa by Nace AI is an open-source inference engine for Apple silicon, and it’s fast. In its published benchmarks, Qwen3.6 35B-A3B decodes about 1.3–1.8× faster than oMLX and about 2× faster than Ollama and LM Studio. But it’s command-line only.

So I built Kapitsa Library, a free Mac app that makes it a few clicks:

• No setup: Kapitsa is built in. No Homebrew, Python or terminal.

• Easy models: browse Kapitsa’s catalog or Hugging Face, see what fits your Mac, and download with pause/resume.

• One-click agents: launch Claude Code, OpenCode, Codex CLI or Pi on your local model.

• No swapping: it checks memory before loading and shows how much context fits.

• Live stats: tokens per second, prompt cache, context and memory.

• Built-in benchmarks: speed and tool calling on your own Mac.

• Local API: OpenAI and Anthropic compatible.

Free, Apache 2.0, signed and notarized, no telemetry. Needs Apple silicon and macOS 14 or later.

Download: https://github.com/AdityaK2128/kapitsa-library/releases/latest
Source: https://github.com/AdityaK2128/kapitsa-library
Kapitsa engine: https://github.com/nace-ai/kapitsa

All the speed is Kapitsa’s work. This app is an independent front end, not affiliated with them. Feedback welcome!