My wife and I met on original okcupid. Not much to say other than the system worked! It’s sad to hear they become tinder-y but I guess it’s hard to compete with the immediacy and dopamine prospects of swiping.
* Models are evolving quickly with high worldwide competition
* Hardware is evolving despite RAM shortages
Is investing a huge sum of money in equipment for local inference a wise use of money? Or are M5 Ultra and equivalently priced local inference hardware future-proof enough to be worth it relative to how the market is evolving? Maybe it’s all a question of what you’d spend otherwise on serverless or dedicated GPU spend…
It is absolutely not worth buying hardware to run models for purely (long term) cost reasons. For open weights models the economies of scale means the cloud beats local significantly and your payback time is like 10 years.
However there are other reasons (e.g. privacy) that might make it worth running locally for some people.
> I think the biggest reason is to own the stack so your model can't be changed out from under you,
The concern would be future regulations that prohibit you from buying a hosted version of the model. Even that could be bypassed with a VPN to another country but it's more work to go through the payments.
As long as there is demand for a model, it will be hosted by multiple providers.
I live in a place where using VPN is illegal and akin to "terrorism" because why would you want to hide what you are doing. Only bad guys hide. So if you use VPN, you are a bad guy.
“Out of the 15 individuals identified, five were minors who were counselled and advised in the presence of their guardians, with emphasis on awareness, lawful digital conduct, and the consequences of violating lawful orders,” he added.
What if the model is hopelessly obsolete, and thus no demand, but I want that specific model? Owning the weights and hardware is not just solving for one problem. It eliminates all the classes of problems that occur outside of your building, if you have a solar and battery setup.
Also, on a more practical basis, what if the way it's served is bad. Maybe I want my specific KV setup, or ultra low quant for entertaining garbage at 200 tk/s
> What if the model is hopelessly obsolete, and thus no demand, but I want that specific model?
You can still find a lot of old and completely outdated models on OpenRouter. The providers can scale serving of models up and down as demand arrives, so models don't generally disappear. They're just kept in the mix and the clouds will allocate hardware to it if someone is willing to pay.
In the odd case that it disappears completely, buying the hardware 2 years from now is probably going to be a better deal. That wasn't true if you selectively check the time period before hardware got expensive, but as new hardware comes out we're going to start seeing Strix Halo and old Apple hardware hit the market as people upgrade. It's already happening.
There is a certain personality type that cannot tolerate any uncertainty and must lock everything in right now against all future possibilities. If you fit that description then there's nothing anyone can say to discourage you from buying your own hardware, but for everyone else I do not recommend buying hardware to self-host LLMs just to save money. I self-host and run a lot of tokens through my setup (non-coding work) but I'm not really saving money.
> There is a certain personality type that cannot tolerate any uncertainty and must lock everything in right now against all future possibilities.
I thought HN banned personal attacks. I'm in this sentence and I don't like it. /s
I just buy the good apple hardware because it's good, and it also happens to run local models. It's not as good for the dollar, don't get me wrong, but I'm not going to develop iOS without a mac, that's even more questionable than buying a strix or whatever.
Also, this makes me wonder if, by using a bicycle generator, and a local model at sufficiently low power consumption, you could directly claim to have produced the text in a really physical way. "Yes, I generated the electrons that made that text work by my own efforts".
You do, there's like 20 providers for any model on openrouter. You can also just spin bedrock or gcp and download the weights for later if you're worried. It's never going to make cost sense when the token rate is so low with how expensive ram is
I think that's overly pessimistic. Here's [1] a video of somebody running it on a ~$6000 rig and getting around 14T/s for complex prompts (about double that for simpler prompts). Payback time is going to depend on your electric cost/consumption. In most domains cloud providers end up charging a significant premium rather than a offering a scale enabled discount, relative to local at retail costs. That will almost certainly end up being the case with LLMs as well, if it isn't already.
Furthermore we continue to follow the path that image gen neural networks took. In that domain hardware requirements reached a peak and then started sharply declining to where we are today where a plain old video card can rapidly generate images that took a supercomputer not that long ago. So it's reasonable to assume that performance of such a system could potentially even increase over time.
With roughly 2.7 million seconds per month, times 14 tokens per second, you are getting 38.5 million tokens a month at most.
That’s less than 164USD worth of GLM5.3 tokens on the inference market. So that 6000 USD rig will take 3 years to break even - and only if it runs continuously.
And this is being generous, as it’s not even taking quantisation into account.
I think the “killer app” is doing inference without sending the data to China or the US. At home it’s overkill but imagine you are an EU consultancy with a lot of client data to work on, or a company/institution with a lot of sensitive data, buying the hardware to make sure the data stays private is a huge benefit. So is that you “own” the model. Its capabilities, price or access don’t change at someone else’s whim.
Some of that is that EU providers need to up their game here.
Needing an EU native option is really the one and only reasonably objection I've heard against using LLMs from the cloud, the rest is tin-foil hat level unless you're actually intending to meddle with the inference or fine tuning or something beyond just querying.
I think if you steel-man what I'm saying, what you're saying falls apart. 14 tokens per second was rare. It only dropped that low in one scenario where he had it single shot an entire game (flappy bird clone) from scratch, with different assets, all self created, and so on. It ended up resulting in the LLM doing stuff like plotting out a some odd 100 item long to-do list, requerying it repeatedly, and so on. And it succeeded.
Also as the video mentions, the guy wasn't very familiar with what he was doing, and so there are almost certainly various optimizations on the config side he could work out, especially as he was using a 5 GPU system, which default configs are probably not well optimized for.
But I think we've rapidly moving along the same path as image gen stuff. Local generation has gone from purely theoretic, to requiring supercomputers to run relatively incapable models, to where we are today - where with a fairly basic high end setup, he's comfortably running a frontier level model. There's definitely an argument for going local that's only growing stronger by the day.
I agree it’s probably not representative token speed. But I do believe the overall observation holds: The monetary value of local inference is bound by the wall clock.
I agree that there are many other reasons than cost alone.
I'm actively uninspired to write high quality code when using Anthropic/OpenAI models given the high chance I'm a customer as well as used as dataset generation tool for them.
But currently cloud does beat costs of hardware ownership, particularly with ridiculously high RAM/GPU/SSD costs....again due to these same companies.
> It is absolutely not worth buying hardware to run models for purely (long term) cost reasons
This is especially true when it's trivial to have the LLM itself write you a script/tool that can rent a GPU node for you (via API calls to providers) and then download and set up an open weight model for you.
I mean, I think it depends. At home 3 of us we use AI for multiple reasons, from coding apps to asking general questions, and if we would have to pay equivalent subscriptions that would be ~1k a year on AI + submitting all your data to external services. I payed around ~8k on 2 DGX Sparks that, at the moment, serves perfectly fine as a ChatGPT/Claude replacement at home (DS4 Flash peaking at ~170 tokens per sec with 6 concurrent sequences), and even once the technology is obsolete for inference in a few years, I will still have 2 pretty powerful machines for whatever I need + some pretty fast NVME Storage. I don't think its a terribly bad idea.
I think the privacy argument that keeps coming up is overrepresented. Certainly ZDR is enough for an absolute majority of use cases? I see so much talk about local inference but I doubt most of it has privacy as a valid argument (not arguing it doesn't exist). It's fun to do things locally though. I've tried it as well but cloud is just faster and cheaper.
These companies have displayed zero respect for everyone's intellectual property getting these models trained.
I think not giving them your complete trust is reasonable! I'm not saying zero trust, and ZDR is fine for most things but I understand the people who don't want to stream their whole codebase out token by token.
Then use other providers hosting open models. Companies and individuals already put their whole code base on the cloud. I'm genuinely interested in privacy-oriented use cases where ZDR is not enough.
ZDR is built on trust. Given that end-to-end encryption fundamentally doesn't work with LLMs, as they need the content to be unencrypted to operate on it[1], you have no way to prove that once your plaintext data is on somebody else's server they aren't doing whatever the hell they please with it. All you have to rely on is their pinky promise that they won't do anything with it. Trust is a valid option, much of our society runs on trust, but you can eliminate the need for trust whatsoever by running on your own hardware.
[1] Yes, I'm aware of experiments to operate on encrypted prompts, but these are only research attempts, not something that could actually be used with frontier models in production.
I'm not that worried about the codebase itself. I'm worried about the fact coding agents poke around the terminal and system so much that there is almost a certainty that some of your other personal data ends up in the context somewhere which is getting logged in to a training dataset by random hosting providers.
Privacy isn’t only, I don’t want anyone to have access to my data. It could also be, I don’t want anyone to know my use case because it’s niche and highly profitable.
I have a Strix Halo and dual 32GB GPUs in my desktop, that sit idle right now, because the electricity to run them and to cool them in 110F weather Texas is currently experiencing pretty much nulls any savings I might see over getting better models from cloud providers. While I mostly use Claude or Codex with subscriptions for agentic work, for API use DeepSeek has usually been my go to, but now I guess it's GLM 5.3 or the Flash version. And, for security work that Anthropic or OpenAI models are likely to refuse, I've been using Kimi K3 (also via subscription, though their subscription is extremely stingy), but I guess GLM is now the one for that, too.
Anyway, yeah, even at the prices I spent on my local AI stuff (I bought before RAMpocalypse really kicked into gear, so I bought old server GPUs for about $350 each and the Strix Halo for a little over $2k) it was never going to pay for itself; I just like to tinker. But, I can't imagine spending today's prices for hardware for local AI.
When the memory shortage ends, I'll be down to the Apple Store (or, more likely, clicking refresh on the Apple outlet every few days). But, until then, there continues to be a glut of cheap and free models in the cloud that are better than anything I can run locally and they're faster, too.
Yeah, I guess, but it feels like there isn't really an opportunity for anyone to do that, given how competitive the market is. If Anthropic decides to demand API rates for everything (which would make my $100/month turn into a few thousand, I guess), I won't be seriously inconvenienced by switching to GPT. And, if both of the major American providers do a pricing collusion and GPT also becomes thousands of dollars a month to use, I can choose between Kimi K3 and GLM and so on. I'd rather use Opus 5 and Fable, but I'm not going to be seriously put out if I can't. We've got three or four open models to choose from that are as good as or better than Opus 4.8, which is Good Enough, and the competition isn't slowing down. We're seeing more new competitive models more frequently than even three months ago.
So, even though there are more models to run locally that can be useful for the stuff I do, it makes less sense now to do so than it did when I got it. There are more extremely cheap options, now, and it seems likely to continue to get cheaper and better and faster, while my local hardware will always be slow and hot and only gets better via software (which has roughly doubled the speed it can run stuff since I got it, but it seems like there's less room for improvement in software now, and even at twice the speed, it still kinda sucks to use local models interactively especially on the Strix Halo).
If they "should" cost 4k in the sense of marginal cost, then you will be spending more running the same at home, because your home hardware will always be less efficient.
There is a big difference in the cost of a 5-nines up time system in a heavily space constrained environment compared to a home hobby white box used for some coding. The GPUs alone cost 10x for the data center versions compared to the gaming versions even with similar specs.
The cost of online services is also largely a result of the cost of training (though hard to say exactly what that number is). Assuming you are using open weight models at home, you aren't paying for the training - someone else is.
> The cost of online services is also largely a result of the cost of training
OpenRouter prices are somewhat simmilar to Antrhopic/OpenAI API prices. So I conclude that the hardware plus operating margin alone can genuinely produce prices way above what you'd pay if you had a subscription.
Of course the primary unkown factor is average token use per subscription. Without that it's all wild speculation.
I wish Texas would write up a regulation allowing 'balcony solar' as I could easily generate 1000-2000w of solar in my small back yard to take a bite out the sizeable cooling bill I have.
Seems like it's easier to ask forgiveness than permission. And, I wouldn't bet on this legislature ever doing anything that would disempower fossil energy or reduce their profits, even a little bit.
There are good reasons you aren't allowed to plug random power generators into the grid. You might be allowed to have your own ones not connected to the grid. Remember that graphics cards run off poorly regulated 12V, although you'd want to regulate it anyway because they're expensive to replace if I'm wrong.
Balcony solar by definition plugs into the grid. It's not just "solar panels that happen to be on a balcony", it's a certain type of self-contained unit that plugs into a regular power outlet.
Nope, because of laws written by energy company lobbyists for the "safety" of consumers, so if you're mixing grid and solar on the same site (even if completely isolated circuits) you have to get all kinds of certifications and permits for inverters and breakers installed by certified technicians that make it cost prohibitive.
Also, many HOAs and local governments place ridiculous restrictions of their own right
I posted more on the other branch of this thread. But cost prohibitive is about all I can find about this and not illegal. Which for some people might be the same result of course.
As I understand it. They banned solar inverters as well as severely tariffing the panels. In some places it's also illegal not to get your electricity from the grid.
So far I don’t regret buying an M1 Max device with 32Gb of RAM. The models available for it keep getting better (running just about okay for interactive use) and 400 GB/s of bandwidth is still considered a lot.
The models are currently improving much faster than the hardware and this doesn’t seem to have plateaued yet.
Try 3.8 27B in MTPLX; I get about 30 tok/s with the same hardware as you. (Although it does use around 90-95W of power, compared to the ~60W that 3.6 35B-A3B uses to generate 55 tok/s. That’s about 3 J/tok instead of 1.)
NGL: I don’t really have a good way to find out right now. It also doesn’t matter that much because the way the models use the tokes varies a lot. Qwen 3.8 is known for overthinking while Muse Glimmer may be a little slower per token, but it uses them very efficiently, caveman style.
Generation speed isn’t the bottleneck anyway, at least on pre M4/M5 devices (the newer chips got significant processing acceleration). It’s prompt processing time. OpenCode’s system prompt can take up to 3 minutes to process, which is why good prompt caching is essential.
For that I use omlx, which can persist the KV cache to disk, chunked so you can reuse parts. This helps with the usability a lot, when an agentic session is warm it runs pretty smoothly. New requests can take a couple seconds (sometimes many, which must be fixable somehow).
So: It’s not fast, but I also don’t find it awfully slow. My use is typically semi-interactive, for fully interactive use you have to wait a bit, but it’s possible. I personally am still regularly amazed that something even close to this is possible on completely local hardware.
That's basically the question I'm trying to answer.
If you're paying Anthropic or OpenAI to use their models, harness, governance, etc., I could see the local inference potentially coming out ahead. They're already starting to ratchet down what your money gets you on their platforms, and that can be expected to continue as the leaders of those companies continue to seek the road to the El Dorado that is being a trillionaire.*
If you're looking to get into the guts of AI development instead of having it handed to you by a provider, that's where it gets murky. I'm wanting to write some sort of agent that does things and get into making outputs consistent in the like, and I'm not sure whether to host something on GCP or buy an M5 Mac.
*Note: El Dorado is a mythical city and many people died trying to find it.
With competition we kind of have guarantee up to what providers can do, they don't have that much control, the most radical thing they can do is to go bankrupt.
Part of it is knowing that whatever sort of enshittification the cloud providers do, my local programming environment won’t ever be less effective than it is today locally. It’s the same reason my entire development stack from editor to compiler is open source. I don’t need to modify it today, but I always must retain the option to do so later.
There are several things I do in my life that only pay off in the event of a big disaster, like an extended internet outage, civil unrest, supply chain disruption, war, etc.
I like to be able to do the things I do even if offline for weeks.
I spent a lot of money for more flash in my iPad Pro so I can keep all of offline wikipedia and OSM in it, for example, along with tons of books. It’s sort of like being a digital prepper. (Being a prepper is a spectrum, from anyone who keeps food in their pantry to people building bunkers under their house - how much you invest is a personal prudence and threat modeling decision.)
Also, privacy. And when I got the Mac Studio the 512GB was only $15k, which is dirt cheap for that much VRAM.
> Part of it is knowing that whatever sort of enshittification the cloud providers do, my local programming environment won’t ever be less effective than it is today locally.
Is that true though? Many of the core LLMs need to be retrained as languages evolve to incorporate changes (language specifics, compilers, tooling, etc.). To some degree this can be handled via context injection in a variety do forms (agents looking up documentation and so on) but inevitably it’s not stationary in time, just as your OSS stack (probably) isn’t (depending on the languages, technologies, and use cases).
So your hardware is to some degree dependent on the good merit of groups like Z or Alibaba or whomever pushing out updated open weight models that dumped loads of capital into to train. You can keep using the existing models but at some point I suspect they’ll start to have more friction due to dated specs in language and so on. Again there are tuning and ways of layering this information on, and in theory you can even do some training on your own but I don’t think it’s as stationary as being portrayed here.
Those updated open weight models may not always be there (updated on new data). The usability of them is probably fairly long to be fair, but I suspect you’re going to see explosion in everything from libraries to languages etc due to LLMs so even the rate of change across your OSS stack may cause these models to be dated quite quickly, at least in the core model which will require layering fixes.
To be clear I’m on the fence thinking about much of the same issues and as close as I am to pulling the trigger, I keep thinking of very valid counter arguments as to why it’s me just wanting this thing I own. Which may be enough.
With every newly released open weight model, the clock on the issues you describe is reset. I can see a marketplace arising for paid updates to common lines of open weight models, which will incentivize those with the hardware to train to fix the problem for those who only have the hardware for inference.
I would say when this comes to pass, we are already 5 years along?
> Part of it is knowing that whatever sort of enshittification the cloud providers do, my local programming environment won’t ever be less effective than it is today locally.
I think this is quite understated. It basically is freedom from a growingly antagonistic relationship between you and some remotely hosted API managed by faceless corporates at the whims of their board, shareholders and governments.. It really is such a mental burden to need to constantly manage this relationship (watermarks, silent downgrades, random false refusals, downtimes, model sunsets, changing ToS's, fucking ads). These companies will need to squeeze you for every cent that they can before open-weight models are simply good enough for the valuable tasks we can throw at them.
To have your own hardware is to no longer have this mental burden.
Only reason to spend a bunch of money on hardware to run LLMs locally is if it's a hobby to you to an extent that even renting the GPUs temporarily won't satisfy you.
Or if you need stuff that APIs don't / can't provide. Or for future proofing your workflows. Running things locally gets you "the same thing" in perpetuity, while APIs might change, models can be deprecated and features can be removed.
Cybersec is also hit and miss, depending on what provider you choose, verification systems and all that jazz. Also, running locally allows you 100% data privacy, in any situation and for whatever usecase you might have. ~100k for hardware for a small team of devs to code locally is not that expensive in the grand scheme of things.
Lastly, local models allow for training / finetuning on your own data and processes. $/tok is not everything for everyone. Sometimes you can take a hit on value / speed if you get something else that matters for you.
Clerky is great. I used it a few years ago and not only was the web product as advertised, they were quick to answer questions and give advice where they could for years after. I hope they keep doing well within Stripe.
> Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal.
> The founding moment was not a new nation, but a Liberty-conceived proposition: all men are equal.
The sample video looks incredible and the ability to keep details consistent for so long is impressive but it _still_ looks screams of AI in every single shot. I can’t put my finger on why, something about the way the rooms are put together, the expressions of the faces, the movements… It’s very unsettling.
I think we as humans are just really good at being able to tell. CGI has been around for decades and yet we can still look at it and say "yeah that was CGI". Uncanny valley I guess?
Wasn’t that a different level of challenge? It was easy to call out the problems with it and recognize gaps to close. With Seedance 2.5, I’m not sure how I’d describe it other than “looking like AI video and all AI video is inherently off-putting”. People smarter than me are working on this but I’d be surprised if it ever actually became tolerable to look at it.
do you remember that article from google where it would hallucinate as if it was on acid/psychedelics and people going crazy saying this is the AI "imagining reality" and then shortly after some Googler went on a very brief media tour saying LLMs were sentient?
its crazy how much people extrapolate, the consensus back then was "cute but we won't get there" then suddenly we got GPT 3.0, 4o, 5.x, seedance
what people really underestimate is how much faster progress is now with AI
A few months ago someone on HN shared Dragonsweeper and it completely hooked me and my wife to the point that we had to agree to break up with it. https://danielben.itch.io/dragonsweeper
It is clearly not effectively communicated if the tool used to write it stamps its signature in such a way that it distracts from the great work they’re doing.
Its only distracts the distractable. Does anyone ever read product announcement blog posts like they were great literature? Ive always just skimmed, theyve always been throwaway writing to me. I’d rather the humans spend more time on the actual creative challenges in a project.
All writing is open to interpretation by the reader.
Some people let punctuation or grammar mistakes ruin an entire text or post for them, because they choose to focus on those flaws, rather than looking past them and taking in the content itself.
"this was written/assisted by AI" is starting to feel like next-gen "this has spelling/grammar mistakes in it, therefore it is invalid".
First time I’ve seen this take, but man it rings true.
I remember having teachers in middle school who seemed draconic to me in their emphasis on proper grammar and punctuation, however as an adult I’m lucky to read an email that contains paragraphs, much less proper punctuation.
Times are changing, and it’s up to the individual to decide how they want to respond to it
This doesn't even account for the fact that while the tech industry's dominant language is English, lots of people in our industry count English as a second (or third/fourth/etc) language, so they might use AI to help them write in English in an effort to be better understood by others.
To arbitrarily dismiss all AI-assisted writing shows a real lack of understanding of the possible reasons one might use it beyond the "lazy author" trope that everyone likes to throw out there.
I think the problem is that too many people use AI to write bullshit. Thus AI writing becomes a cue for bullshit.
For people who have internalized AI writing as a cue for bullshit, it is very difficult to read obvious AI writing without constantly being cue-ed that the thing they're reading is bullshit. Even if it's not.
Crazy fast but the the way all header text sizes change briefly flash between values every time you switch tabs would drive me nuts if I was responsible for this.
Obsidian is my favorite new tool. I started a new job last week and decided to spin it up on day one to help me onboard and learn their system. It’s been wildly successful, the missing piece I needed to really turn Claude Code into the learning and documentation tool I dreamed about.
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