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Comment by swatcoder | Hacker News Reader

Compare to the cost of professional-grade tools in other trades and craft hobbies.

Sure, $4000 can be a lot of if you're a casual hobbyist or are struggle to meet everyday lifestyle costs, but it's definitely not "insane" if this is the trade you make your living from or if you've established a lifestyle that affords disposable income for your hobbies.

And for some people, $4000 for a device you have complete control over and can repurpose and tinker with to your own needs and curiosities is a much much more justifiable expense than a $200/mo rental for some narrow-access tool that somebody else controls.

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That's only half the reason it's expensive.

The other reason is that it would likely take years to spend $4000 (plus the real cost of electricity) worth of tokens on a 3rd-party provider that's running a similar limited, DS Flash type model. By that time, the hardware will be obsolete, assuming it's still operational.

> it would likely take years to spend $4000 (plus the real cost of electricity)

Since that cluster only yields 20-30 tok/s on that size of model, at least a decade before the hardware breaks-even with current token costs, and that's not counting electricity. Assuming continued downward pressure on token prices, and the cost of electricity, it never pays for itself.

As a counterpoint, my homelab/home-LLM hardware has appreciated in value by about 60% since I bought it.

Of course, it's not real unless I sell, and the value will eventually go down, but so far I have significant paper profits.

Also, DeepSeek token prices are continuing to _increase_, not decrease.

> DeepSeek token prices are continuing to _increase_

One increase does not a trend make. And the current crop of models are now undercutting deepseek flash...

You can't possibly think that it's going to get cheaper and cheaper to pay for tokens though. Right? Have you seen what's happening with Codex/Claude subscriptions? Deepseek raising API prices.. We've been getting subsidized tokens for some time now and as the hardware costs skyrocket these labs/people with inference compute are going to continue to clamp down.

> You can't possibly think that it's going to get cheaper and cheaper to pay for tokens though. Right?

Absolutely I do. Each generation of open-weight models has come with significant efficiency improvements, and there are significant hardware gains on the horizon: both increasing competition from Chinese chip manufacturers, and new custom silicon from the established players. And unlike Anthropic and OpenAI, most of the pure inference providers aren't massively leveraged - the more hardware they can bring online, the cheaper they can serve tokens.

$40,000 GPU is like few pennies in sand. Only mildly hyperbolic. But a GPU fresh out of fab is $2000 after ASML, TSMC and inputs get their 50-75% margin, then somehow $40k laundered through US financialization / Nvidia margins. Commoditized GPUs shouldn't cost more than 1-2% current price once there's competition.

We’re getting subsidized training. The inference is not a loss leader. And since providers can run hardware at 100 percent 24/7 their per token cost is going to be far below mine, regardless of how long I’m willing to wait for a token to come out.

I don't understand how people don't consider this.

Plus you're spec'd out of near-SOTA level in months.

The only reasons to actually do this are a) you have a lot of dispensable income and are a hobbyist/tinkerer, b) you have real, legitimate privacy concerns or, relatedly, c) you're doing something you don't want to get flagged

"you don't want to get flagged"

Ding!

What is actually getting you flagged by the openweights inference providers? Thus far I haven't hit any of the reverse engineering or infosec guardrails that Anthropic is so keen on

While I'm sure some of the open weight providers do this as well, I think the comparison is frontier labs v local inference.

I'm not sure that is the comparison - the OP is planning to run an open weights model locally, the obvious comparison would be paying a hosted provider to run the exact same model

Not everything is about pure cost. Maybe I don't want to sell my soul supporting the frontier labs because they are straight up pure evil?

I barely see a difference between buying the hardware that feeds (and often colludes with) those labs, at least not as a moral stance.

Even if you trained your own model, you'd be committing some of the same sins, paying for the same hardware that drove it, etc. But if you're using some open model, you're standing on the shoulders of the same corrupt giants.

I feel like when people say this is due to moral reasons, it's to justify an expensive hobby.

I mean it's entirely a personal decision. I didn't mean to come across judgemental, if anyone uses the frontier models I don't hold it against them. But for me personally I am very against the frontier labs in general. This technology is too powerful to not be at least open weights, but preferably open source.

I also differentiate using their tech and paying them money. I don't think using their models, or perhaps using models derived from them as inherently evil. I just do not want to actually contribute to their bottom line in any way. Even if that means a slower ramp up of AI in general. In my opinion we could move slower.

I understand Nvidia is working with the labs to assist them to buy more hardware through financing and other deals. But ultimately I do not view that as the same as contributing directly to their P&L.

> it never pays for itself.

Exactly; its a development box for fiddling with GPU hardware with a large amount of video-addressable memory. It's not an inference box, really, though it's neat that I can at all!

But if you can use cloud models, why wouldn’t you use SOTA? For 2400 USD or less per year you can get pretty huge amounts of benefit out of that (though at the whim of whoever you are giving the money to).

> The other reason is that it would likely take years to spend $4000 (plus the real cost of electricity) worth of tokens on a 3rd-party provider

That's just a one-dimensional thought! Your own hardware gives you complete control, and it doesn't time you out for 4 hours, unlike those vendors.

I am pretty confident that given a $200 subscription on any of the big labs, you're getting $4000-$8000 per month in subsidized tokens... do what you wan't with your dough... and I too have a spark that I got really early (October 2025), but no, economically it does not compare to what's runnable locally in terms of quality from the frontier models. Economically, it looks like for as long as there are subscriber plans, you're better off renting.

Before getting the spark, I was just using a google colab account, their $49 dollar plan allows you access to h100's and I can run qwen there in a Jupyter notebook... and if I really need that web front end I can just use cloudeflair/tailscale/the local ssh client to reverse tunnel it.

The cloud stuff is definitely a much better economic value, but I would argue:

1. You learn a lot more running this stuff yourself (especially since you can poke at its internals if you're interested or watch the reasoning chain.) Just being a consumer of this stuff doesn't really teach you much about it other than model & harness specific tricks that become obsolete pretty quickly. (IE, your Claude.md from 6 months ago probably needs a rewrite). Which is fine, I don't think you're going to be "left behind" if you're not a hardcore AI enthusiast or anything (I'm not), but as a guy that's always been interested in computer science I want to see how it ticks.

2. You can't really depend on this subsidization lasting forever IMO. I know the financials thing has been beaten to death but I guess I'm in the camp that it's good to be in control of your tools so that you can go elsewhere if the economics change.

I like to check in with ccusage pretty frequently, and honestly like if I were paying API prices for Claude I'd probably be paying thousands a month.

3.privacy

Any organisation or individuals not wanting to have their sensitive data flowing away (either because of trade secret or data protection laws)

Or good old fashioned privacy.

There’s no law or business advantage preventing me giving my financial transaction and medical info to Google/Anthropic/OpenAI but I just don’t want to.

I am also not sure I would choose to use the cheap and easy to run at home model, given a choice. The marketing copy says this is a frontier model, but it's not. Sol and Mythos are the frontier right now. GLM 5.3 Flash simply isn't. I'd rather use the frontier model as they waste less of my time than even Opus.

Anecdotally, ~$500-1500/month token spend at API OpenAI/Anthropic pricing seems pretty realistic for full-time engineers at companies with "liberal but not unlimited" LLM spend policies.

This is of course anecdata. I know plenty of outliers, too. I know a principal engineer who uses many multiples of the number I quoted above. I am sure we also know many people making do with much much smaller budgets as well, via all kinds of well-discussed methods.

But, "$500-$1500 per month per full-time developer" is just kind of the personal mental baseline I use when making my decisions with regards to thinking about whether any of this makes any economic sense.

This should be obvious but with a model running on local hardware you can do your own RLHF and mod its behavior however you see fit. With cloud hosted models you can't. A few years ago when the models were smaller there were people undoing the guardrails, censorship, and general lobotomization with some form of a RLHF training. You can't do that on larger models unless you have the hardware like this person does.

Notice all the comments saying like "omg why so expensive so just use the API??". It's a trick for lockin even with, so called, "open" models. Keep trying to run them locally, keep undoing the lobotomies, mod model behavior so that they work for you and do what you want vs only what someone else says they're allowed to do.

> You can't do that on larger models unless you have the hardware like this person does.

Or just rent something substantial for like $4/hr on runpod or w/e to do that.

My gripe is this persons compute is wasteful and makes it harder for me to buy something with like 64gb ram to do normal work and run containers while I keep using cloud models.

Someone else calculated the break even being 10 years, it’s just dumb. And I think it’s clear there won’t be a big rug pull anymore, there are too many open models and providers now.

I love my Spark-like, but even for training you're better off using Vast or Runpod or whatever to rent cloud compute. Much faster and cheap as hell, to be honest.

I do set up my initial runs and likes like quantisation-aware-distillation on my Spark-like to test it out and get it working, so it has value! But its not "worth" it other than its fun hardware to tinker with, IMO.

With multiple 200 a month subs you are getting a multiple of those subsidized tokens. At least if you tabulate at retail api prices.

This rent in the era of expensive hardware thing is not exclusive to inference.

I’ve needed x86 architecture for windows builds recently and have just hemmed and hawed over buying a decent windows 11 box.

I can’t make the math work against Azure instances.

I can spin up a nice one for build deallocate,spin up something cheaper for QA and then turn that off.

I can build all the devops around that, with a number of passes, with a skills based interface so working with the cloud is not too bad.

The only thing that still has me thinking about it is the prospect of price is going up even more, which is acid as far as I know.

And I’m hopefully going to need this x86 stuff enough that I don’t wanna wish I had gotten one for that high prices now.

It's "insane" compared to the $1,500 it should have cost before the RAM crisis

For $200/mo you either have a SotA model you can’t run on those devices or you have a cheaper model where you pay less than $200 or have a really big amount of tokens without the energy costs and the risk of failing machine

Yeah, in any other profession where you need to buy a van to drive stuff around, you easily spend similar amount of money on capital investment.

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