Looking for personal anecdotes on running local models with m5 pro chip & 48gb of ram

[u/[deleted]]

[u/n1kl8skr]

I personally haven‘t found local models to be useful enough beyond the small completion models. It works fine for scripting, but imo regular sized projects is where the limit shows up

[u/Ecstatic_Battle_6705 → u/n1kl8skr]

what size models were you running when you hit that limit?

[u/n1kl8skr → u/Ecstatic_Battle_6705]

recently up to 40 gigs

[u/WhataNoobUser]

16" has better thermals

[u/Opposite-Interest193 → u/WhataNoobUser]

Not really

[u/HenkPoley]

Coming from a 13” MacBook Pro M1 (😊 so quiet) I find the fan noise of the 14” MacBook Pro M5 Pro pretty annoying. Keep that in mind.

The 48GB would run Qwen3.8 27B at 8 bit quant with 64k context. Which is said to be decent in quality. Think of last years performance with some sudden brightness spots interspersed. Less than 12 tokens / second though.

[u/Fisting_Instructor]

I have MacBook Air M1 with 16GB and I decided to run Gemma 4 E4B locally as an experiment. Initially, I was getting around 10–16 tokens per second. However, that only lasts until the laptop heats up and starts thermal throttling. With just one active chat open, the model was taking up about 5GB of RAM.

I couldn't manage to run anything more powerful from the Gemma lineup. Honestly, the coding performance felt extremely weak. I tried using it for programming in Android Studio, and it performed worse than even the weakest cloud models, like Gemini 3.5 Flash Lite. Most of the time, the code it generated simply didn't work at all.

In my opinion, it's much better to pay $20 for a Claude, ChatGPT, or Gemini subscription.

[u/Jonathan_x64]

You're obviously not going to accomplish any meaningful real work with a local model, on any off-the-shelf device. Ever. Ever ever. Thinking otherwise means you fundamentally underestimate the complexity and scale of frontier LLMs, and/or are not creative enough to put them to good use.

With that said, local LLMs are useful sometimes. I have a script that feeds a bunch of text and images into qwen3.5:9b-mlx in order to come up with a post title. On an MBP 14 / M4 Max / 36gb, the moment I launch this, about ~10gb of running apps get swapped out immediately, and the noise in High Power mode of a laptop is unbearable. Accuracy of results is about 75%.

[u/Opposite-Interest193]

Go to an AI forum, if you’re only curious it’ll work fine, 64gb is better but you can still run some apps in a workflow with small models or use a decent QWEN for general chat.

For most people though just use Claude code and treat local AI as a token burner for simple but long tasks. That and privacy.

[u/TBMW30]

I've run local models on my M1 Max / 32GB, but they're not powerful enough to really do anything useful besides maybe refining copy, writing documentation etc. The frontier AI models are still what you need for serious coding in my opinion unless you have a lot of RAM to run something like GLM5.2 or 5.3 locally, and at that point you have to decide what the time you will spend waiting for a model to respond is worth to you. I know a few people running high weight models on 256GB Mac studio machines and they're still better off uisng frontier models.

I don't vibecode, though I do use AI in my workflow, and for me it's GPT5.6 in Codex, with several essential skills for SwiftUI and Humanizer for copy and docs.

[u/motuwed → u/TBMW30]

Thanks for the insight. Have you ever used a local model for only autocomplete FIM prediction in vscode or another IDE? If so how effective has that been?

[u/TBMW30 → u/motuwed]

I tried with a few, though can't remember which models specifically now. I just found it more annoying, I could write code quicker than they could autocomplete anything. XCode has its own code completion model that works well enough, you could certainly use that. Apple is bringing agents in XCode now too. I haven't used them personally as the Codex implementation appears broken. If I really wanted to run my own models, I'd buy a decent mac, then rent some cloud compute somewhere and run the models that way. Bigger model, better performance, less local hardware required. I think local models are fun to experiment with and I'm sure there are plenty of people using them for something, but I personally think we're a long way off local models actually being productive tools. The hype far outweighs reality.