> 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.
Me. Currently using Claude Max for personal coding projects. I've been on Claude's $20 plan and would run out of tokens. I don't want to give my money to OpenAI. So far these projects have not returned their value back to me, but I am viewing it as an investment in learning best pratices with these coding tools.
> If that's you, know that you can get a LONG way on the $20/month plans from OpenAI and Anthropic.
> 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.
These are the same people, by and large. What I have seen is users who purely vibe code everything and run into the limits of the $20/m models and pay up for the more expensive ones. Essentially they're trading learning coding (and time, in some cases, it's not always faster to vibe code than do it yourself) for money.
bit the bullet this week and paid for a month of claude and a month of chatgpt plus. claude seems to have much lower token limits, both aggregate and rate-limited and GPT-5.2 isn't a bad model at all. $20 for claude is not enough even for a hobby project (after one day!), openai looks like it might be.
And as a hobbyist the time to sign up for the $20/month plan is after you've spent $20 on tokens at least a couple times.
YMMV based on the kinds of side projects you do, but it's definitely been cheaper for me in the long run to pay by token, and the flexibility it offers is great.
"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.
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
LMStudio? No, it's the easiest way to run am LLM locally that I've seen to the point where I've stopped looking at other alternatives.
It's cross-platform (Win/Mac/Linux), detects the most appropriate GPU in your system and tells you whether the model you want to download will run within it's RAM footprint.
It lets you set up a local server that you can access through API calls as if you were remotely connected to an online service.
The tradeoff is a somewhat higher learning curve, since you need to manually browse the model library and choose the model/quantization that best fit your workflow and hardware. OTOH, it's also open-source unlike LMStudio which is proprietary.
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.)
> I'm curious what the mental calculus was that a $5k laptop would competitively benchmark against SOTA models for the next 5 years was.
Well, the hardware remains the same but local models get better and more efficient, so I don't think there is much difference between paying 5k for online models over 5 years vs getting a laptop (and well, you'll need a laptop anyway, so why not just get a good enough one to run local models in the first place?).
I agree with everything you said, and yet I cannot help but respect a person who wants to do it himself. It reminds me of the hacker culture of the 80s and 90s.
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
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.
Eh, these things are trained on existing data. The further you are from that the worse the models get.
I've noticed that I need to be a lot more specific in those cases, up to the point where being more specific is slowing me down, partially because I don't always know what the right thing is.
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.
If you want to do it cheap, get a desktop motherboard with two PCIe slots and two GPUs.
Cheap tier is dual 3060 12G. Runs 24B Q6 and 32B Q4 at 16 tok/sec. The limitation is VRAM for large context. 1000 lines of code is ~20k tokens. 32k tokens is is ~10G VRAM.
Expensive tier is dual 3090 or 4090 or 5090. You'd be able to run 32B Q8 with large context, or a 70B Q6.
For software, llama.cpp and llama-swap. GGUF models from HuggingFace. It just works.
If you need more than that, you're into enterprise hardware with 4+ PCIe slots which costs as much as a car and the power consumption of a small country. You're better to just pay for Claude Code.
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.
> 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.
These are the same people, by and large. What I have seen is users who purely vibe code everything and run into the limits of the $20/m models and pay up for the more expensive ones. Essentially they're trading learning coding (and time, in some cases, it's not always faster to vibe code than do it yourself) for money.
(I also have the same MBP the author has and have used Aider with Qwen locally.)
YMMV based on the kinds of side projects you do, but it's definitely been cheaper for me in the long run to pay by token, and the flexibility it offers is great.
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
It's cross-platform (Win/Mac/Linux), detects the most appropriate GPU in your system and tells you whether the model you want to download will run within it's RAM footprint.
It lets you set up a local server that you can access through API calls as if you were remotely connected to an online service.
- Cross-platform
- Sets up a local API server
The tradeoff is a somewhat higher learning curve, since you need to manually browse the model library and choose the model/quantization that best fit your workflow and hardware. OTOH, it's also open-source unlike LMStudio which is proprietary.
but people should use llama.cpp instead
and why should that affect usage? it's not like ollama users fork the repo before installing it.
I mean, what's the point of using local models if you can't trust the app itself?
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.)
Well, the hardware remains the same but local models get better and more efficient, so I don't think there is much difference between paying 5k for online models over 5 years vs getting a laptop (and well, you'll need a laptop anyway, so why not just get a good enough one to run local models in the first place?).
I've noticed that I need to be a lot more specific in those cases, up to the point where being more specific is slowing me down, partially because I don't always know what the right thing is.
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.
Cheap tier is dual 3060 12G. Runs 24B Q6 and 32B Q4 at 16 tok/sec. The limitation is VRAM for large context. 1000 lines of code is ~20k tokens. 32k tokens is is ~10G VRAM.
Expensive tier is dual 3090 or 4090 or 5090. You'd be able to run 32B Q8 with large context, or a 70B Q6.
For software, llama.cpp and llama-swap. GGUF models from HuggingFace. It just works.
If you need more than that, you're into enterprise hardware with 4+ PCIe slots which costs as much as a car and the power consumption of a small country. You're better to just pay for Claude Code.