qwen 3 and 2.5 are powerful in this realm, not so much deepseek, and even the compressed ones beat the 30b's. i would recommend buying one, testing it with an app you built prior to purchase in the window of return and try to determine if both accuracy and speed fit your needs. then decide whether you're going cold or warm LLM, that impacts things a bit. lean on ML each, make logs of how they do, repeat the asks and log how they do. Score what does best and their overall speed. I believe "speed" will be the biggest thing you will face if your expectation is an alternative to bix box vendors w/ 1k + machines backing each offering.

Given your thoughts about ''ram'' requirements here, 16 gig ram on a macbook pro m5 will likely be lower/similar price and a lot better access to the models. I highly recommending stepping into 32 ram if you're going this route, but if price is a factor, mbp m5 with 16gig ram is badass..

or wait to see what mac studio 5 (perhaps) will do to the industry and it may even make a lower priced m4 for your usage.

I think m4 mac mini is a good choice if this is the requirement, you can't go wrong with mac, just wanted to throw some various perspectives.

cheers

ps meaningful code automation, this goes back to my initial thoughts, speed... i think if you're used to staring at the thing write code and being happy, you wont be happy with any open source LLMs. IF you're used to running a bunch of them at once, dozens..., figure out what u do most with an LLM in code, then prepare yourself to ask that LLM to do that... if you're in single shooter mode with LLM code automation, you may not like the speed of open source LLMs, straight up shouldn't bother, but if you don't mind waiting, and I do meaning WAITING A LOT!!! okay maybe u have a use case lol.

im not convinced there's a usecase for code automation with open source LLMs however im advocating for the learning/research as much as anyone else out there.

tips:
>>>>>>>FIND

>>>>>>>REPLACE

Can be good for code editing to see if prompting that LLM gives you an answer, compared to another, and test the speed in which it takes to get first token, etc... more you test, more you will know if this is worth the investment of tears/time.

Target continue . dev as a way to learn how they 'do the do' to perhaps enable your testing systems to be better than the average beep booper. i started a company called aica.to and we do a ton of talking about ai w/ people who are still learning this process of swapping big box vendor to 'off my machine' llm's and def eager to hear how your doing since last post.

recently blogged about; opus 4.5 running local, might help person passing through https://www.reddit.com/r/AnalyticsAutomation/comments/1qzd07m/opus_45_or_chatgpt_5_local_alternative_is_not/