> I realized I looked at this more from the angle of a hobbiest paying for these coding tools. Someone doing little side projects—not someone in a production setting. I did this because I see a lot of people signing up for $100/mo or $200/mo coding subscriptions for personal projects when they likely don’t need to.

Are people really doing that?

If that's you, know that you can get a LONG way on the $20/month plans from OpenAI and Anthropic. The OpenAI one in particular is a great deal, because Codex is charged a whole lot lower than Claude.

The time to cough up $100 or $200/month is when you've exhausted your $20/month quota and you are frustrated at getting cut off. At that point you should be able to make a responsible decision by yourself.

I don’t think I’ve ever read an article where the reason I knew the author was completely wrong about all of their assumptions was that they admitted it themselves and left the bad assumptions in the article.

The above paragraph is meant to be a compliment.

But justifying it based on keeping his Mac for five years is crazy. At the rate things are moving, coding models are going to get so much better in a year, the gap is going to widen.

Also in the case of his father where he is working for a company that must use a self hosted model or any other company that needed it, would a $10K Mac Studio with 512GB RAM be worth it? What about two Mac Studios connected over Thunderbolt using the newly released support in macOS 26?

https://news.ycombinator.com/item?id=46248644

This story talks about MLX and Ollama but doesn't mention LM Studio - https://lmstudio.ai/

LM Studio can run both MLX and GGUF models but does so from an Ollama style (but more full-featured) macOS GUI. They also have a very actively maintained model catalog at https://lmstudio.ai/models

I'm curious what the mental calculus was that a $5k laptop would competitively benchmark against SOTA models for the next 5 years was.

Somewhat comically, the author seems to have made it about 2 days. Out of 1,825. I think the real story is the folly of fixating your eyes on shiny new hardware and searching for justifications. I'm too ashamed to admit how many times I've done that dance...

Local models are purely for fun, hobby, and extreme privacy paranoia. If you really want privacy beyond a ToS guarantee, just lease a server (I know they can still be spying on that, but it's a threshold.)

"This particular [80B] model is what I’m using with 128GB of RAM". The author then goes on to breezily suggest you try the 4B model instead of you only have 8GB of RAM. With no discussion of exactly what a hit in quality you'll be taking doing that.

In my experience the latest models (Opus 4.5, GPT 5.2) Are _just_ starting to keep up with the problems I'm throwing at them, and I really wish they did a better job, so I think we're still 1-2 years away from local models not wasting developer time outside of CRUD web apps.

I wouldn't run local models on the development PC. Instead run them on a box in another room or another location. Less fan noise and it won't influence the performance of the pc you're working on.

Latency is not an issue at all for LLMs, even a few hundred ms won't matter.

It doesn't make a lot of sense to me, except when working offline while traveling.

Cline + RooCode and VSCode already works really well with local models like qwen3-coder or even the latest gpt-oss. It is not as plug-and-play as Claude but it gets you to a point where you only have to do the last 5% of the work

If privacy is your top priority, then sure spend a few grand on hardware and run everything locally.

Personally, I run a few local models (around 30B params is the ceiling on my hardware at 8k context), and I still keep a $200 ChatGPT subscription cause I'm not spending $5-6k just to run models like K2 or GLM-4.6 (they’re usable, but clearly behind OpenAI, Claude, or Gemini for my workflow)

I was got excited about aescoder-4b (model that specialize in web design only) after its DesignArena benchmarks, but it falls apart on large codebases and is mediocre at Tailwind

That said, I think there’s real potential in small, highly specialized models like 4B model trained only for FastAPI, Tailwind or a single framework. Until that actually exists and works well, I’m sticking with remote services.

I appreciate the author's modesty but the flip-flopping was a little confusing. If I'm not mistaken, the conclusion is that by "self-hosting" you save money in all cases, but you cripple performance in scenarios where you need to squeeze out the kind of quality that requires hardware that's impractical to cobble together at home or within a laptop.

I am still toying with the notion of assembling an LLM tower with a few old GPUs but I don't use LLMs enough at the moment to justify it.

I’ve been using Qwen3 Coder 30b quantized down to IQ3_XSS to fit in < 16gb vram. Blazing fast 200+ tokens per second on a 4080. I don’t ask anything complicated, but one off scripts to do something I’d normally have to do manually by hand or take an hour to write the script myself? Absolutely.

These are no more than a few dozen lines I can easily eyeball and verify with confidence- that’s done in under 60 seconds and leaves Claude code with plenty of quota for significant tasks.

I hope hardware becomes so cheap local models become the standard.

The resource contention point is real, running local models alongside Docker and your actual dev environment is where this can fall apart. One comment here mentions running models on a separate box to avoid impacting your dev machine. That's the right idea but idle GPUs are everywhere the infrastructure to actually tap into them is what's missing. Currently building something along these lines. https://sporeintel.com/

Isnt the math of buying Nvidia stock with what you pay for all the hardware and then just paying $20 a month for codex with the annual returns better?

This is not really a guide to local coding models which is kinda disappointing. Would have been interested in a review of all the cutting edge open weight models in various applications.

Are people really so naive to think that the price/quality of proprietary models is going to stay the same forever? I would guess sometime in the next 2-3 years all of the major AI companies are going to increase the price/enshittify their models to the point where running local models is really going to be worth it.

What are you doing with these models that you’re going above free tier on copilot?

Not worth it yet. I run a 6000 black for image and video generation, but local coding models just aren't on the same level as the closed ones.

I grabbed Gemini for $10/month during Black Friday, GPT for $15, and Claude for $20. Comes out to $45 total, and I never hit the limits since I toggle between the different models. Plus it has the benefit of not dumping too much money into one provider or hyper focusing on one model.

That said, as soon as an open weight model gets to the level of the closed ones we have now, I'll switch to local inference in a heartbeat.

your premise would've been right, if memory wouldn't skyrocketed like 400% in like 2 weeks.