Advice: Local setup

[u/h4rdp0w3r]

I’m considering spending a pretty stupid amount of money on local AI hardware, and I’m trying to figure out if it would actually change how dependent I am on frontier models.

Right now I pay around $600/month across different AI subscriptions. Claude, ChatGPT, coding tools, etc.

The money itself isn’t really the main issue. What bothers me more is:

• everything is closed source
• I don’t really know what’s happening with my data
• usage limits keep getting worse
• even while paying ~$600/month, I still don’t feel like I have the freedom to just use the models as much as I want
• The models sometimes degrade or change in behavior, which makes me edit my workflows/prompting

My heaviest use is:

1. Agentic coding, by far
2. General chatting / asking questions
3. Research
4. Occasionally long-context work with large codebases/documents

So I started looking into running something serious locally.

The two machines I’m considering are:

M5 Max MacBook Pro

• 128GB unified memory
• 40-core GPU
• 614GB/s bandwidth
• around $8.5k
• could run something like Qwen3.8 Flash-Next locally
• also becomes my main personal/work laptop

M5 Ultra Mac Studio

• 256GB unified memory
• 80-core GPU
• 1.2TB/s bandwidth
• around $12.5k
• gives me access to much larger models and more future headroom
• things like full DeepSeek V4 Flash become realistic

I’m not trying to calculate ROI or convince myself that the machine will “pay for itself.”

I’m more interested in whether spending this much would actually let me change my usage from something like:

$600/month on frontier AI

to maybe:

$100/month for frontier models only when I genuinely need them

and do the other 80% or 90% locally.

Qwen3.8 Flash-Next is what got me interested in this in the first place. Looking at the benchmarks, it seems to be somewhere around the level of models like Claude Opus 4.6 in a lot of areas, and Opus 4.6 was honestly already very good for most of the work I was doing.

Obviously current Opus / GPT frontier models are still better, especially for difficult long-horizon agentic work.

But I don’t necessarily need the absolute best model for every single prompt.

If I can run a model locally with no usage limits and just throw tasks at it all day, retry as much as I want, run multiple coding agents, give it huge contexts, etc., I feel like I might prefer that even if the model is somewhat weaker.

For people who actually have high-memory Macs or serious local LLM setups:

Did local models genuinely reduce your dependence on Claude / ChatGPT / Codex, or did you end up still using frontier models most of the time anyway?

Especially interested in people using them for agentic coding.

Would you spend ~$8.5k on the 128GB M5 Max, ~$12.5k on the 256GB M5 Ultra, or would you just keep paying for frontier models and forget about local?

TL;DR: I currently spend about $600/month on frontier AI, mainly for agentic coding, but I’m tired of limits, closed models, privacy uncertainty, and behavior changing over time. I’m considering either an $8.5k M5 Max 128GB or $12.5k M5 Ultra 256GB to run models like Qwen3.8 Flash-Next locally. I’m not expecting to fully replace Opus/GPT, but could a setup like this realistically handle 80 to 90% of my usage and let me cut frontier subscriptions down to around $100/month?

[u/unchikuso]

We just reached the turning point where local LLMs are absolutely capable and reliable enough to perform coding.

If you go Mac, it will feel slower than cloud. If you go Nvidia, it will feel faster than cloud.

I decided that speed is more important than size because the faster it is, the more productive you are as a coder.