You aren't going to get nearly as much token usage locally from DGX Sparks or even M5 Ultra (though it might be close, unsure would need to get my mittens on it to clarify).
You will get around 2-4 concurrent streams of aggregate tokens at best for such a model and around 0.5B output tokens per month assuming you use loops and run it when you are sleeping. That's 500 (per mill) * 0.5$ = 250$ only at most.
Then there is maintanence and efficiency costs due to electricity usage and such, any down time, etc.
You will be lucky if you can squeeze more than 200$ of value out of it in a month.
I don't think people should buy local hardware for money reasons, by the time you will pay off a 10K USD machine, 2-3K USD machine will catch up and beat it by a significant margin.
Unless your expectation is that we will be in hardware winter for the next 10+ years. At 200$ per month it will take around 200 * 50 = 10k, that is, 50 months, so around 4-5 years.
Again assuming you are making the most of your hardware somehow, very hard to do in practice.
I don't recommend people to use compute as investment or payoff thing, but if you have the money to burn and can afford it why not, maybe with some software optimizations it will be cheaper but then again Z.ai is currently offering 50% discount and providers will offer cheaper rates for sure.
But either way you will never be able to burn more than 200$ worth of token on a cheap hardware device, because inference becomes more profitable the more you scale it up, you have separate prefill and decode engines/systems, and a lot of nuance, but assume for every 10x increase in infra you increase margins by 5-10%.
So from 10K to 100K to 1M to 10M to 100M.. I don't think this curve continues beyond 100M but I have no idea about that scale unless some AI lab is interested in hiring me lol.
So a 100M infra will have ~30% better margins than you at 10K, then there is software optimizations but that's cheap enough, though some of it is only viable at scale.
Either way assume 10K is the price of privacy if you really want to buy it. Don't worry about making the most out of the usage, you will always be in a net loss but I would assume for you 10K doesn't matter.
I generally agree — go local for the hobby/tinkering, privacy, and control (ie not getting refused by an AI to defend and secure your own network and codebase; as HuggingFace has seen).
But whether you make a loss or not depends on how hardware prices and resell values go though.
I have spent ~$50K on local AI hardware. The market value of that hardware is about ~$80K right now.
So the maths is working out for me so far. I see it as a call option on compute.
> I have spent ~$50K on local AI hardware. The market value of that hardware is about ~$80K right now.
That's easy because no matter what IT hardware you bought, it's worth more now than it was two years ago. That's something that's unprecedented, never happened before, and as soon as we get flood gates open on ram manufacturing OR when the AI bubble pops, all IT HW deprecation norms will return and making a profit by buying something IT will vanish.
I had a GPU server four years ago. Had I not sold it like three years ago with 2x price I bought it, it would be likely something like 5x the price nowadays.
I really really miss filling my home rack with old enterprise stuff. All I want is this hardware winter to end.
yeah i mostly agree, especially compared to subsidized subscription cost.
But for a heavy user who has enough work to be done so that the box runs almost 24/7 at say 50tok/sec, the math gets interesting against API prices.
And it can be interesting compared to subscription in the sense that you don't have the quota anymore. That means there's probably a lot of things you're not doing because of the quotas that you could do now.
It depends heavily on the tok/sec obviously and the very best solution financially remains subscriptions. But the idea remains entertaining and not that disconnected from reality
At 50tps for single stream you are going to get 50 * 60 * 60 * 24 * 30 = 130M out tokens of GLM 5.3 Flash...
That's less than what 40$ at current API rates... So if you are willing to pay 200$ per month you will get much better limits paying API rates.
You can't run large Kimi K3 models on 10K worth of hardware either way, you need to spend like 50K USD minimum.
Just pay for the API rates or get a low cost provider that uses higher batching, you can get shittier tps but much better prices, probably go as low as 20$ for as much usage as you can ever get from a 10K USD machine from GLM 5.3 Flash...
The issue is nothing expensive runs on these devices and cheap stuff isn't worth running locally, eletricity costs ~12cents/kwh in us iirc, so at 330W M5 Ultra will burn around 8 * 0.12 = ~1$ per day extra in electricity so the electricity is going to cost you the same as the API rates(30$ per month).
I truly don't think you are accounting for the costs here properly. But again if money truly doesn't matter it's much better for privacy and better than paying one of the shady AI labs who are doing god knows what with your data.
Your point isn't lost on me, but a few other considerations:
1) Rates are theoretically discounted for GLM 5.3 Flash right now, by 50%.
2) Hardware costs have continued ascending with no sign of letting off, so it's unlikely that a DGX Spark depreciates to zero in one year.
3) Compare performance in terms of difficult tasks/$ over the last 6 months, 3 months, etc. Open weights are a ratchet. In terms of intelligence per $, a Spark is never going to be a worse deal tomorrow than it is today, at least until the entire platform is replaced or obsoleted.
71 days ago the best model you could run on two Sparks was an aggressive Q3 quant of Qwen 3.5 397B (AA 34). 70 days ago it was a mixed-quant of GLM 5.2 (AA 53). 30 days ago it was full fat DeepSeek 4 Flash (AA 53). Today it's GLM 5.3 Flash (AA57) and/or Qwen 3.8 Next (Unknown). Sometime this week it will likely become mixed-quant GLM 5.3 (AA 60).
So in < 80 days we have almost doubled the benchmark score. And that curve is still accelerating. If you view it as "cost per token of model vs API" then yes it's a bad deal. If you view it as "cost of task per $" then it has almost doubled in value in less than 3 months. All of this, imo, API and hardware, is still massively underpriced.
> 2) Hardware costs have continued ascending with no sign of letting off, so it's unlikely that a DGX Spark depreciates to zero in one year.
If someone told me that costs for X will keep increasing because they have been increasing rapidly in the last 1.5 years, but they have a history of continuously decreasing for decades before that.
I am not sure if I will take anything they say serious, I am not sure if it's HN or AI but people are delusional if they think compute costs will keep increasing from now on...
Either AI will be really good, hence compute and everything will materially depreciate or it won't be much better than it is today and token volumes will plateau compared to compute.
For instance the amount of token compute that's to come online in 6-12 months is several times what we have today...
Second 3) Compare performance in terms of difficult tasks/$ over the last 6 months, 3 months, etc. Open weights are a ratchet. In terms of intelligence per $, a Spark is never going to be a worse deal tomorrow than it is today, at least until the entire platform is replaced or obsoleted.
This is a bad take because again this assumes DGX Spark will not depreciate in price, we will have something better for far cheaper surely in the next couple years. M5 Max & Ultra are already arguably it, but will have to see.
> 71 days ago the best model you could run on two Sparks was an aggressive Q3 quant of Qwen 3.5 397B (AA 34). 70 days ago it was a mixed-quant of GLM 5.2 (AA 53). 30 days ago it was full fat DeepSeek 4 Flash (AA 53). Today it's GLM 5.3 Flash (AA57) and/or Qwen 3.8 Next (Unknown). Sometime this week it will likely become mixed-quant GLM 5.3 (AA 60).
This has nothing to do with DGX Spark's value, if models get cheaper the API costs also go down, this is not a defensible argument to cost to value.
Are people on HN really not thinking straight?
Tldr; no matter how you do the math compute is only getting more valuable because of a temporary crunch, don't expect this to continue permanently, sure you maybe able to time it and make money but so could you in stocks this is not for investments. Further second hand hardware sells for cheaper than sticker price, outside of a bubble..
And models getting cheaper == APIs getting cheaper == your hardware becoming worse value as your electricity & maintanence costs still remain.
I am not saying local models don't have their place but if someone is trying to use this logic to justify their purchase then I wish them all the best, as someone who is actively working on AI compute/inference/hardware stuff I personally don't have this level of courage.
But this is not a sound investment strategy that if something is going up and seems like it might keep going up, especially when investing in heavily depreciating assets like compute.