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Experimenting with Local LLMs on macOS

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Re: Experimenting with Local LLMs on macOS

#81

So far I've not run into the kind of use cases that local LLMs can convincingly provide without making me feel like I'm using the first ever ChatGPT from 2022, in that they are limited and quite limiting. I am curious about what use cases the community has found that work for them. The example that one user has given in this thread about their local LLM inventing a Sun Tzu interview is exactly the kind of limitation…

I use, or at least try to use local models while prototyping/developing apps.

First, they control costs during development, which depending on what you're doing, can get quite expensive for low or no budget projects.

Second, they force me to have more constraints and more carefully compose things. If a local model (albeit something somewhat capable like gpt-oss or qwen3) can start to piece together this agentic workflow I am trying to model, chances are, it'll start working quite well and quite quickly if I switch to even a budget cloud model (something like gpt-5-mini.)

However, dealing with these constraints might not be worth the time if you can stuff all of the documents in your context window for the cloud models and get good results, but it will probably be cheaper and faster on an ongoing basis to have split the task up.

Re: Experimenting with Local LLMs on macOS

#82

So far I've not run into the kind of use cases that local LLMs can convincingly provide without making me feel like I'm using the first ever ChatGPT from 2022, in that they are limited and quite limiting. I am curious about what use cases the community has found that work for them. The example that one user has given in this thread about their local LLM inventing a Sun Tzu interview is exactly the kind of limitation…

I use a local LLM for lots of little things that I used to use search engines for. Defining words, looking up unicode symbols for copy/paste, reminders on how to do X in bash or Python. Sometimes I use it as a starting point for high-level questions and curiosity and then move to actual human content or larger online models for more details and/or fact-checking if needed.

If your computer is somewhat modern and has a decent amount of RAM to spare, it can probably run one of the smaller-but-still-useful models just fine, even without a GPU.

My reasons:

1) Search engines are actively incentivized to not show useful results. SEO-optimized clickbait articles contain long fluffy, contentless prose intermixed with ads. The longer they can keep you "searching" for the information instead of "finding" it, the better is for their bottom line. Because if you actually manage to find the information you're looking for, you close the tab and stop looking at ads. If you don't find what you need, you keep scrolling and generate more ad revenue for the advertisers and search engines. It's exactly the same reasons online dating sites are futile for most people: every successful match made results in two lost customers which is bad for revenue.

LLMs (even local ones in some cases) are quite good at giving you direct answers to direct questions which is 90% of my use for search engines to begin with. Yes, sometimes they hallucinate. No, it's not usually a big deal if you apply some common sense.

2) Most datacenter-hosted LLMs don't have ads built into them now, but they will. As soon as we get used to "trusting" hosted models due to how good they have become, the model developers and operators will figure out how to turn the model into a sneaky salesman. You'll ask it for the specs on a certain model of Dell laptop and it will pretend it didn't hear you and reply, "You should try HP's latest line of up business-class notebooks, they're fast, affordable, and come in 5 fabulous colors to suit your unique personal style!" I want to make sure I'm emphasizing that it's not IF this happens, it's WHEN.

Local LLMs COULD have advertising at some point, but it will probably be rare and/or weird as these smaller models are meant mainly for development and further experimentation. I have faith that some open-weight models will always exist in some form, even if they never rival commercially-hosted models in overall quality.

3) I've made peace with the fact that data privacy in the age of Big Tech is a myth, but that doesn't mean I can't minimize my exposure by keeping some of my random musings and queries to myself. Self-hosted AI models will never be as "good" as the ones hosted in datacenters, but they are still plenty useful.

4) I'm still in the early stages of this, but I can develop my own tools around small local models without paying a hosted model provider and/or becoming their product.

5) I was a huge skeptic about the overall value of AI during all of the initial hype. Then I realized that this stuff isn't some fad that will disappear tomorrow. It will get better. The experience will get more refined. It will get more accurate. It will consume less energy. It will be totally ubiquitous. If you fail to come to speed on some important new technology or trend, you will be left in the dust by those who do. I understand the skepticism and pushback, but the future moves forward regardless.

Re: Experimenting with Local LLMs on macOS

#83

Earlier quoted context omitted.

Not sure about the Mac Pro, since you pay a lot for the big fancy case. The Studio seems more sensible. And of course Nvidia and AMD are coming out with options for massive amounts of high bandwidth GPU memory in desktop form factors. I like the idea of having basically a local LLM server that your laptop or other devices can connect to. Then your laptop doesn’t have to burn its battery on LLM work and it’s still loc…

> Not sure about the Mac Pro, since you pay a lot for the big fancy case. The Studio seems more sensible Oh wow, a maxed out Studio could run a 600B parameter model entirely in memory. Not bad for $12k. There may be a business in creating the software that links that box to an app on your phone.

Perhaps said software could even form an end to end encrypted tunnel from your phone to your local LLM server anywhere over the internet via a simple server intermediary.

The amount of data transferred is tiny and the latency costs are typically going to be dominated by the LLM inference anyway. Not much advantage to doing LAN only except that you don’t need a server.

Though the amount of people who care enough to buy a $3k - $10k server and set this up compared to just using ChatGPT is probably very small.

Re: Experimenting with Local LLMs on macOS

#84

Earlier quoted context omitted.

> Not sure about the Mac Pro, since you pay a lot for the big fancy case. The Studio seems more sensible Oh wow, a maxed out Studio could run a 600B parameter model entirely in memory. Not bad for $12k. There may be a business in creating the software that links that box to an app on your phone.

Perhaps said software could even form an end to end encrypted tunnel from your phone to your local LLM server anywhere over the internet via a simple server intermediary. The amount of data transferred is tiny and the latency costs are typically going to be dominated by the LLM inference anyway. Not much advantage to doing LAN only except that you don’t need a server. Though the amount of people who care enough to bu…

> amount of people who care enough to buy a $3k - $10k server and set this up compared to just using ChatGPT is probably very small

So I maxed that out, and it’s with Apple’s margins. I suspect you could do it for $5k.

I’d also note that for heavy users of ChatGPT, the difference in energy costs for a home setup and the price for ChatGPT tokens may make this financially compelling for heavy users.

Re: Experimenting with Local LLMs on macOS

#85

I don't think we're anywhere close to running cutting-edge LLMs on our phones or laptops. What may be around the corner is running great models on a box at home. The AI lives at home. Your thin client talks to it, maybe runs a smaller AI on device to balance latency and quality. (This would be a natural extension for Apple to go into with its Mac Pro line. $10 to 20k for a home LLM device isn't ridiculous.)

This is what I’m doing with my amd 395+.

I’m running docker containers with different apps and it works well enough for a lot of my use cases.

I mostly use Qwen Code and GPT OSS 120b right now.

When the next generation of this tech comes through I will probably upgrade despite the price, the value is worth it to me.

Re: Experimenting with Local LLMs on macOS

#86
DEVONThink 4’s support for local models is great and could possibly contribute to the software’s enduring success for the next 10 years. I’ve found it helpful for summarizing documents and selections of text, but it can do a lot more than that apparently.

https://www.devontechnologies.com/blog/20250513-local-ai-in-...

Re: Experimenting with Local LLMs on macOS

#87

I believe local llms are the future. It will only get better. Once we get to the level of even last year's state of the art I don't see any reason to use chatgpt/anthropic/other. We don't even need one big model good at everything. Imagine loading a small model from a collection of dozens of models depending on the tasks you have in mind. There is no moat.

It's true that local LLMs are only going to get better, but it's not clear they will become generally practical for the foreseeable future. There have been huge improvements to the reasoning and coding capabilities of local models, but most of that comes from refinements to training data and training techniques (e.g. RLHF, DPO, CoT etc), while the most important factor by far remains the capability to reduce hallucinations to comfortable margins using the raw statistical power you get with massive full-precision parameter counts. The hardware gap between today's SOTA models and what's available to the consumer are so massive that it'll likely be at least a decade before they become practical.

Re: Experimenting with Local LLMs on macOS

#88

So far I've not run into the kind of use cases that local LLMs can convincingly provide without making me feel like I'm using the first ever ChatGPT from 2022, in that they are limited and quite limiting. I am curious about what use cases the community has found that work for them. The example that one user has given in this thread about their local LLM inventing a Sun Tzu interview is exactly the kind of limitation…

I have tried a lot of different LLMs and Gemma3:27b on a 48gb+ Macbook is probably the best for analyzing diaries and personal stuff you don't want to share with the cloud. The China models are comically bad with life advice. For example, I asked Deepseek to read my diaries and talk to me about my life goals and it told me in a very Confucian manner what the proper relationships in my life were for my stage of life and station in society. Gemma is much more western.

Re: Experimenting with Local LLMs on macOS

#89

So far I've not run into the kind of use cases that local LLMs can convincingly provide without making me feel like I'm using the first ever ChatGPT from 2022, in that they are limited and quite limiting. I am curious about what use cases the community has found that work for them. The example that one user has given in this thread about their local LLM inventing a Sun Tzu interview is exactly the kind of limitation…

something like rewind or openRecall can use local LLMs for on-device semantic search.
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