But why Local LLM? How does this make economic sense vs API?
Posted by Thistlemanizzle@reddit | LocalLLaMA | View on Reddit | 38 comments
Hey guys, come fight me: how do you justify local LLMs from a value perspective?
I’m not talking about privacy, censorship, offline use, control, or hobby value. I’m trying to figure out whether it actually makes financial sense to buy a machine that can run local LLMs well, purely for the savings.
### Example comparison
- \~$2,500 128GB Strix Halo box
- \~$3,700 128GB M4 Max Mac Studio
vs. Minimax 2.7 on OpenRouter:
- **Input:** $0.30 / 1M
- **Output:** $1.20 / 1M
- **Cache read:** $0.059 / 1M
Using a rough **3:1 input:output** ratio, I get:
- **3M input + 1M output = $2.10**
- **Effective rate = $0.525 / 1M total tokens**
Amortized over **36 months**, that seems to imply break-even around:
- **132M total tokens/month** on the **$2,500** machine
- **196M total tokens/month** on the **$3,700** machine
That makes it seem like very cheap APIs are hard to beat on pure dollars.
So for those of you running local, what is the economic case?
The biggest possibilities I can think of are:
- enough volume, including shared or concurrent use, to break even on the hardware
- avoiding runaway API bills from badly configured agents or workflows
Would love to hear from people who have actually run the numbers.
SexyAlienHotTubWater@reddit
You're assuming the price of APIs isn't going to go up. Demand for inference is exploding - the supply of server GPUs is not. Prices will go up.
jikilan_@reddit
You cant just look into the aspect of economic only. How about behaviour and availability of the model? They can be updated and become worst or pulled off. Local is consistent and tested.
Murgatroyd314@reddit
It makes perfect economic sense if you already bought a decently powerful computer for other purposes. My marginal cost for any and all local AI work is zero, which beats any API anywhere.
IsopodInitial6766@reddit
You can't price compare to a model that won't exist in 18 months APIs deprecate models on their schedule, local rigs let you freeze yours
NNN_Throwaway2@reddit
It doesn’t, because API tokens are being sold at a loss lol. How do people still not understand this? The inference industry is not a real business.
Thistlemanizzle@reddit (OP)
If you can run decent LLMs locally then why can't the API providers sell access for cheap? e.g Gemma 4 31B
CreamPitiful4295@reddit
Can’t on pure economics. Much cheaper to use APIs. However, if you want total control over your tools, data locality, security or just are a hobbyist, local is where it’s at. And, then there are some of us who would like a backup llm for when the economics of the big AI companies cause them to disappear. :)
Thistlemanizzle@reddit (OP)
You think Minimax is unsustainable?
ttkciar@reddit
The entire LLM inference industry, as it exists today, is unsustainable. This has been discussed multiple times in this sub, and I recommend availing yourself of Reddit's search feature.
When the AI industry hits its third bust cycle, local LLM users will be well-positioned to weather the industry disruption, price-tier restructuring, and commercial consolidation.
Thistlemanizzle@reddit (OP)
I feel like it's a race to the bottom. Sure, OpenAi, Anthropic and Google will stop subsidizing. But what is there a moat when smaller cheaper to provide inference models can get 80℅ of the effectiveness?
In the worst case scenario, wouldn't it be better to just wait for that transition and buy the hardware then?
ttkciar@reddit
That sounds good "on paper", but in practice there are thresholds of quality which make or break a model for a given task.
For example, there have been a lot of codegen models since 2022, but the first one which met my criteria for worthiness of use was GLM-4.5-Air, just last year. Models which are even slightly worse than Air simply aren't worth it to me.
Admittedly some of this is a matter of personal preference and specific use-cases. As a professional software engineer with 47 years of programming experience, my standards for codegen competence are different from someone who has only been coding for a few years.
The point is that "80% as good" might be good enough for some people / use-cases, and not good enough for others. A model which fails to meet your standards for acceptable minimum competence is just a waste of time and disk space.
That having been said, some people's standards for inference competence are ridiculously low. There are a surprisingly many people who appear to be perfectly happy with Qwen3.5-9B, which will run on a potato.
That does make a degree of sense, yes, and I'm sort of doing that already. For a couple of years, I dorked around with local LLM technology without spending a cent on hardware for it. I already had systems (servers, workstation, laptop) I was using for other things, and simply repurposed those to learn LLM tech skills.
As I got better at using LLM technology and the models became much more competent at a given size, I did pick up some very low-budget GPUs to plug into my existing hardware, so that I wasn't limited to pure-CPU inference -- a 32GB MI60 for $800, then a 16GB V340 for $60, and most recently a 32GB MI50 for about $250.
These let me use mid-sized models at good speed, which are well-suited to some purposes, but mostly I see them as training wheels for gaining experience and skills to eventually apply to beefier GPUs, when they come down in price.
As you implied, after the next AI industry bust cycle, those beefier GPUs should become a lot more expensive. I do anticipate buying "serious" compute infrastructure then.
In the meanwhile, any time I use an inference service's API instead of local inference, I am robbing myself of opportunities to develop my LLM tech skills, and risk forming a dependency upon a service under someone else's control, which can change at any moment in unpredictable ways.
It's a trade-off, and on the most part I have chosen the "use local inference" side of that trade-off.
If you really need better inference (faster and/or more competent) than you can host on your hardware, though, and it can't wait, and you're not willing to buy better hardware, and you are tolerant of the risks inherent to paid inference services, then it does make sense to use an inference service. That is a totally valid choice, and it makes good sense for some people.
We're not in this sub because local inference is always the best choice for everyone; we are in this sub because local inference is the choice we have made.
Thistlemanizzle@reddit (OP)
Thanks! This to me is the best reply.
I was operating under the fantasy I could justify what is really a toy purchase under the guise it would pay for itself.
One rebuttal. If you are able to run models locally you are happy with, then wouldn't the API providers be able to serve up that model so cheap it would take forever to break even on local? That is, if you can run it, so can they and since it's an open marketplace, it will be a race to the bottom.
ttkciar@reddit
You are quite welcome. I'm glad you found an answer which works for you.
Well, that's not a straightforward calculation, because there are unpredictable factors. For example, a lot of people have been reporting recently that Claude has "gotten a lot stupider" suddenly. You can calculate the cost/benefit and break-even for local vs service by making assumptions like "Claude will always be this price" or "Claude will always be this competent", but those assumptions might or might not hold up.
In some cases, commercial models go away entirely, too. OpenAI has retired GPT-4, and never deployed a model which replaces it to all of their customers' satisfaction, for example.
Probably so, yes, especially since they are operating at a net loss, but I cannot say from experience. It would be interesting to see what providers are offering Qwen3.5-9B inference, at what performance, and at what price.
The break-even cost vs local inference for that could be calculated based on a $30 8GB GPU (if you don't already have one, but do have a desktop you could plug it into) plus your local cost of electricity.
Karnemelk@reddit
use cloud models to tune/build your local LLM, then when they do some dumb thing or squeeze their models, you have your local personalized clone
shanehiltonward@reddit
Old math is cute.
With the advances in memory management and video ram management, you can run better models with RTX hardware and less physical ram.
If you already own the hardware (like many in this sub), they expense was paid long ago. Running an LLM is just another compute job for your existing hardware.
Finally, if you are a CEO, you'll want to run your own LLM (check yesterday's news about Claude and ChatGPT).
Thistlemanizzle@reddit (OP)
But as models become more cheaper/efficient to run, won't the economic proposition still be on the API side too?
shanehiltonward@reddit
Also, you don't have to buy another computer the following year, but you WILL be paying for tokens every year.
ttkciar@reddit
Only as long as the API providers continue to operate at a net loss, as explained here:
https://www.wheresyoured.at/the-subprime-ai-crisis-is-here/
Eventually investors will expect returns on their investments.
LLM inference providers will either provide them with that at a much, much higher subscription price points (without losing their customers, somehow) or they won't. In the latter case, without net profitability and no more rounds of investment they will either be acquired by profitable companies or cease operations.
dinerburgeryum@reddit
We're already seeing the cracks in the consumer pricing model for hosted LLMs: Anthropic dropping support for *Claw harnesses, OpenAI killing Sora... these things are expensive, and we're all riding high right now on subsidized inference, but nothing lasts forever. Best to get ahead of it and figure out how to do it without cost speed bumps.
Extending that, OpenRouter is the Wild West of inference platforms. Where are you tokens going? Who is reading them? Is there proprietary client information in there? PII? Can you answer these questions with any certainty? Almost certainly no, which takes it off the table for client work. (Might not be relevant for everyone, but certainly relevant for me.)
Further, there's definitely the matter of inference providers swapping out models and quantizations without explicit user consent. We're constantly hearing "this model sucks now!" or "it's so dumb now!" and of course; we can't build enough compute, so even the big houses have to squeeze out whatever they can. Much better to create an arguably less-powerful platform that you control than be at the whims of someone else's cost center.
theplayerofthedark@reddit
Honestly, there is none. Its mostly about privacy / beeing independent of big tech. Im trying M2.7 on strix halo right now and its just not that usable. While staying with the def. 98gigs of vram your capped at about \~70k ctx. PP speed is also reallly rough. I use my framework for so much else that the AI part of it is a nice to have. Buying these for the sole usecase of running LLMs seems really tough sell unless you need / care about the privacy or just the novelty of running it yourself
Thistlemanizzle@reddit (OP)
Damn, I thought the 128GB would be enough. I definitely can't swing a $6K Mac Studio 256GB purchase.
theplayerofthedark@reddit
On Linux you can get more then 98gb vram allocated with some workarounds, but the performance is (atleat for my usecase of coding) not that amazing.
ProfessionalSpend589@reddit
It is. You want to play with new hardware and try an LLM service at the same time, but you have modest requirements?
Buy the hardware and skip the service for a 3 years of savings.
StardockEngineer@reddit
"very cheap APIs are hard to beat on pure dollars" Yup, that's true. That's not the only consideration for most of us, though. Especially me, where the main consideration is "this is really fun to do at home"
Randommaggy@reddit
The cheap price is a trap. They will start squeezing soon.
Thistlemanizzle@reddit (OP)
Its cool as hell. I will agree with that all day.
Front_Eagle739@reddit
Now add in engineering time for "this workflow worked yesterday and now it doesnt because they changed something on the server and now its different"
Randommaggy@reddit
This is the main factor for me. If I build an automation I want it to keep working as it originally did until I change it.
Randommaggy@reddit
If you get hooked on cloud LLMs you will get fucked over hard when they need to try to turn a profit.
Your pain will be the oil that lubricates those wheels.
I only use things that I can self host beyond experiments to avoid getting sodomized financially down the line.
It may be less capable but I can use it as much as I want for what I want without having to be concerned about my data getting stolen. And it won't randomly change which is a reliability factor all by itself.
SLxTnT@reddit
If your goal is a working end result as fast as possible and nothing else, go with an API.
If you enjoy the process of learning, no subscriptions/unknown prices, privacy, consistency, or like owning the hardware. Own hardware. You also forgot to take into account being able to sell the hardware in your calculations.
ladz@reddit
"Economics" includes many factors besides money. Privacy and provable repeatability are also factors and are missing when you use some one else's service instead of your own.
Heavy_Boss_1467@reddit
you think running local model is only about money ?
Thistlemanizzle@reddit (OP)
I want someone to justify my purchase on the basis it will save me money. The other stuff like true privacy, latency are not really a big deal for right now.
flyingbanana1234@reddit
for me definitely about all things ai image,video,rags, etc
running it uncensored and nobody being able to take it away via price hikes
PlayfulLingonberry73@reddit
Few things:
If you want to run off the network and don't want to share your data to other.
You need LLM and smaller models can deliver your needs. Then you have unlimited API calls.
You are a YouTuber and you want to make videos of every models. However useless those are like 0.5tps
e979d9@reddit
It would make sense for a group of friends pooling resources maybe, or a team of a few people in an enterprise setting
Look_0ver_There@reddit
So your post is basically saying: "If we discount every possible reason for why people would do it, then it doesn't make economical sense!"
It kind of reminds me of that Monty Python bit of "What have the Romans ever done for us?!"
ElectroSpore@reddit
Will complete depend what you are using them for?
Run a week or a month of your workloads on the per /1M token plan and tell us how much it really costs.
Context windows, how big your prompts are, RAG etc will EAT tokens like MAD.