Ask HN: M5 MacBook Pro buyers, worth spending the $$ to maybe run LLMs local? | Hacker News Reader

Ask HN: M5 MacBook Pro buyers, worth spending the $$$ to maybe run LLMs local?

11 pointstpurves12 comments

To anyone upgrading their daily driver Mac this year, are you considering going to a Max + high memory config? eg. with the hope (now or in near future) of being able to do usefully run agents/LLMs locally on your main machine?

Or is the few extra thousand dollars difference between a base and max-spec MBP still just better spent on literally any other practical option (like different harware, remote hardware, cloud AI subscriptions or credits). Or wait to see if there will be an M5 Studio or what inferencing performance next year's M6 may bring?

I am tempted, but even with some new models getting skinnier and more efficient, I am not sure moore's law and the M5 generation is quite there yet to be worth the trouble?

What call's are ya'll making and why?

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I went from an M1 16GB to M5 Pro 48GB. I'm running Qwen 3.5 with it locally. I've been sending it and Opus 4.6 the same prompts in identical copies of codebases, using Claude Code for both (using ollama to launch with Qwen). It is about 4x slower than sending the request to Opus. The results are not nearly as good either.

One task that I sent to both was to make a website to search transcription files generated from video files that were also provided. I wanted to have the transcriptions display and be clickable. When clicked have the video skip to that point in play. The Opus website looked nice, and worked well. Qwen couldn't get the videos to play.

Now, for day-to-day tasks, the M1 wasn't a slouch, but the M5 Pro is still a big step forward in terms of performance.

That's helpful insight. My prediction is that as it keeps getting more expensive for the big players to run these models, we will start to see some kind of hybrid workload where they offload some of the work to your computer for smaller agents while keeping the orchestration and planning running in the data centers.

So I think the investment in the extra hardware is worth it, even if you don't currently plan on running LLMs locally.

I mean I get you but, I also know that there are better ways to operationalize local AI. Your POV still remains as super helpful context. I feel like a lot of local-vs-cloud discussion stops at “slower and worse,” but the useful part is understanding where it broke down like model quality, tool use, runtime setup (and not stop at the task performance between the two in it self).

I have an M1 Pro MBP. I keep being tempted to upgrade, but when I look at the improvements being made, it makes me continue to wait. In day to day use, I’m not hurting at all with the M1 Pro and it seems like I’ll be able to get so much more in a couple years if trends continue.

When it comes to local AI, I’m of the option that this is where things should go in the long-term. However, I want to see the market mature more to understand what that will look like. I don’t want to buy today in hopes of something down the road.

So pretty much all my buy signals are telling me to kick the can down the road.

This site was posted a couple weeks back. You can select the M5 Max from the list and see how it would run various local models. That may help make the decision.

https://www.canirun.ai/

Ditto. On an M1 mac mini since 2021. Cheap little machine. Chugs the typical web developer workload like a champ.

I will try to get 10 years use out of it. 2031 will be time to evaluate the next machine.

Yep, I am in same boat. 32GB M1 Pro myself, and ya still a very solid machine. Thanks for that link.

You should ask r/LocalLLaMa, they have more benchmarks such as this [0]. As an aside, the other comments in this HN comment section are useless, I mean, one is talking about using cloud models when the question is specifically about local models, which have various reasons to exist beyond cloud models; and another one is about not buying any more Apple products, like, that's irrelevant to the question at hand.

[0] https://www.reddit.com/r/LocalLLaMA/comments/1rzkw4x/m5_max_...

I upgraded from M3 Air to M5 Pro 14". Not for LLM, but for faster build times. The disk access time is much faster on M5 Pro. My build time (iOS/Android) was reduced by more than 50%. So, that alone is worth the upgrade.

I thought about it for a moment, but the real reason I got the new M5 Pro with 64GB is to be able to run several large projects concurrently in Docker envs.

I didn't go for a Max chip because I value the better battery life on the Pro more than I value the additional GPU cores.

Personally, I think until the LLMs start to plateau, it will always be more valuable to run a frontier LLM vs just a very capable local LLM. I have no idea when that will happen, so I simply decided to not overbuy the hardware now.

I’m running local models with a maxed out M4 but I find local models only useful and reliable for trivial tasks and sensitive items like database optimization work. Local LLMs just don’t come anywhere close to Claude or Codex for heavy work.

I want to buy a Mac Studio m4 max.. I can barely run an 8b model on my device right now and I can't even dream about fine tuning.

I have never come close to hitting a limit using Codex cli with my $20/month ChatGPT subscription all day for five months

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