I just used it on a Apple M4 MacBook Pro with 48GB RAM with llama.cpp and Pi to help diagnose an infinite looping request in a React Server component on a Next.js application. After about 10+ hours of digging, it has apparently found a bug in the Next.js framework, with an example app that replicates the bug, and a fix for now to disable prefetch in the Link component. I had in my prompt asked it to discover the root cause of the bug and propose a fix, but I did not expect it to dig this deep.
Neutral
It's just a Macbook Air with the base M4 and 16gb ram, but I'm hosting the models on a Mac Studio with M1 Ultra and 64gb ram that I had purchased when it came out. I get about 45-55 tokens per second with this setup. I think I could get more if I spent some time fiddling with the parameters, but I don't really know what I'm doing there so I've just left most of it on oMLX's defaults.
Neutral
It's just an M4 MacBook Air with 16gb ram – probably incapable of running models itself. I actually run the models on my Mac Studio which is an M1 Ultra with 64gb, and oh-my-pi on my laptop is configured to use the models over the local network.
Likes it
I purchased mine for approximately $4400 AUD before the price hikes. That unit is now ~$5100 AUD. I use my MBP essentially as my workstation, it's almost always plugged in. I have a MBA (M4, 24GB RAM) that I picked up for ~A$1500 or so, and that's an amazing daily driver. I don't do local LLM inference on that unit, I can just hit my own APIs (via LM Studio) on the MBP over Tailscale.
Likes it
In case someone would like to know what these are like on this hardware, I tested Gemma 4 32b (the ~20 GB model, the largest Gemma model Google published) and Gemma 4 gemma4:e4b (the ~10 GB model) on this exact setup (Mac Mini M4 with 24 GB of RAM using Ollama), I livestreamed it: https://www.youtube.com/live/G5OVcKO70ns The ~10 GB model is super speedy, loading in a few seconds and giving responses almost instantly. If you just want to see its performance, it says hello around the 2 minute mark in the video (and fast!) and the ~20 GB model says hello around 5 minutes 45 seconds in the video. You can see the difference in their loading times and speed, which is a substantial difference. I also had each of them complete a difficult coding task, they both got it correct but the 20 GB model was much slower. It's a bit too slow to use on this setup day to day, plus it would take almost all the memory. The 10 GB model could fit comfortably on a Mac Mini 24 GB with plenty of RAM left for everything else, and it seems like you can use it for small-size useful coding tasks.
Doesn’t like it
I think (without having done extensive research) that some sort of Apple hardware is your best bet right now. Apple hasn’t raised RAM upgrade prices [1] (although to be fair their RAM upgrades were hugely inflated before the crunch) and their high memory bandwidth means they do inference faster than most consumer GPUs. I have an M4 MacBook Air with 24 GB RAM and it doesn’t feel sufficient to run a substantial coding model (in addition to all my desktop apps). I’m thinking about upgrading to an M5 MacBook Pro with much more RAM, but I think the capabilities of cloud-hosted models will always run ahead of local models and it might never be that useful to do local inference. In the cloud you can run multiple models in parallel (e.g. to work on different problems in parallel) but locally you only have a fixed amount of memory bandwidth so running multiple model instances in parallel is slower. [1] https://9to5mac.com/2026/03/03/apple-macbook-price-increase-...
Neutral
Side note: this local-first strategy works well but requires some beefy hardware with gobs of ram, at least in my line of work with java and rails microservices. My laptop is an M4 with 64gb ram. I honestly wish I had the 128GB model...
Doesn’t like it
I've been analyzing my Obsidian vault using local LLMs that I run via Apple's mlx_lm. I'm on an M4 MacBook Pro with 48GB RAM. The results are ... okay. The biggest problem is that I can't run some of the largest models on my hardware. The ones I'm running (mostly Qwen 3 at different numbers of parameters and quantization levels) often produce hallucinations. Overall, I can't say this is a practical or useful setup, but I'm just playing around so I don't mind. That said, I doubt SOTA models would be that much better at this task. IMO LLM generated summaries and insights are never very good or useful. They're fine for assessing whether a particular text is worth reading, but they often extract the wrong information, or miss some critical information, or over-focus on one specific part of the text.
Neutral
I just used GPT-OSS-120B on a cross Atlantic flight on my MacBook Pro (M4, 128GB RAM). A few things I noticed: - it’s only fast with with small context windows and small total token context; once more than ~10k tokens you’re basically queueing everything for a long time - MCPs/web search/url fetch have already become a very important part of interacting with LLMs; when they’re not available the LLM utility is greatly diminished - a lot of CLI/TUI coding tools (e.g., opencode) were not working reliably offline at this time with the model, despite being setup prior to being offline That’s in addition to the other quirks others have noted with the OSS models.
Likes it
13in MacBook Air (new M4, with 24GB RAM) and Mac Mini as desktop the best setup I’ve ever had. I debated between the 13in and 15inch MacBook Airs and am so glad I went with the 13.
Neutral
I have R1:1.5B running on my 8gb ram M4 mac mini. Dont know where I would use it, as it is too weak to solve actual problems, but it does run.
Likes it
M4 Mac Mini with 16GB RAM is doing a "good enough" job of editing 6k raw footage in Premiere for my team. I'm surprised to say I'm content with the 16GB of ram so far. Edit: This is in contrast to my M1 Macbook Air with 16GB of ram which would stutter a lot during color grading. So definitely feeling the improvement.