32 ms·

Here’s an experiment: purchase an anthropic pro max subscription for $200/m. Now go buy the hardware to run DeepSeek’s equivalent. In a year, who spent more?

ericd 1mo ago
That's not apples to apples on almost any dimension.
egeozcan 1mo ago
In normal times in which hardware used to depreciate (lately that's not the case and HW even appreciates, but let's not get distracted), if you calculate only with depreciation costs, plus the fact that when you have such a setup, it'd take many 200$ subs to cover your lack of limits in the other, I think it'd not be a clear victory for any side. If you just ask "who spent more in the first year" (100% depreciation) then even with 5-6 max accounts, buying HW will be a couple of times more expensive. But when does it make sense to ask that question? Maybe the SotA models will need better hardware so your investment will not be useful after a year or you'd need very expensive upgrades? But then (as in Fable case) subscribers need to spend more too.
srcreigh 1mo ago
It’s not so clear after 5 years that you’ll come out ahead. You’ll have spent $20k. The apple computer owner will probably be running local models that are better than today’s frontier on the same hardware. Idk where you live, but where I am running the M5 Ultra Mac Studio at max rated power 24/7 for a month costs C$42. The considerations against Apple hardware are 1) hardware advancements 2) early access to the best models. But it’s really not that clear. (The other guy who thought hosted models on openrouter are cheap has spent $100k in 5 years.)
- SXX 1mo ago
  > The apple computer owner will probably be running local models that are better than today’s frontier on the same hardware. Hardware is not magically getting more memory or bandwidth. Believing there will be some magical optimizations to compensate for it is just dellusion.
  - chlorion 1mo ago
    Then explain how equal parameter size models can grow in capability every few months or year?
  - srcreigh 1mo ago
    Open weight models have been getting better/smaller every year. Also, from what I can tell, MLX inference is not as well optimized as CUDA, and the M5 Ultra has additional kinds of AI compute which is unavailable on other M models. With the massive 1.2 TB/s 512GB Mac studios coming out, I think MLX will get a lot more attention. In short: Todays models should run faster next year, and next year's models should also be more efficient.
EagnaIonat 1mo ago
Depends on what you plan to do. You don't need frontier models to summarise or create an email.

ericd 1mo ago

That's not apples to apples on almost any dimension.

egeozcan 1mo ago

In normal times in which hardware used to depreciate (lately that's not the case and HW even appreciates, but let's not get distracted), if you calculate only with depreciation costs, plus the fact that when you have such a setup, it'd take many 200$ subs to cover your lack of limits in the other, I think it'd not be a clear victory for any side. If you just ask "who spent more in the first year" (100% depreciation) then even with 5-6 max accounts, buying HW will be a couple of times more expensive. But when does it make sense to ask that question? Maybe the SotA models will need better hardware so your investment will not be useful after a year or you'd need very expensive upgrades? But then (as in Fable case) subscribers need to spend more too.

srcreigh 1mo ago

It’s not so clear after 5 years that you’ll come out ahead. You’ll have spent $20k. The apple computer owner will probably be running local models that are better than today’s frontier on the same hardware. Idk where you live, but where I am running the M5 Ultra Mac Studio at max rated power 24/7 for a month costs C$42. The considerations against Apple hardware are 1) hardware advancements 2) early access to the best models. But it’s really not that clear. (The other guy who thought hosted models on openrouter are cheap has spent $100k in 5 years.)

SXX 1mo ago
> The apple computer owner will probably be running local models that are better than today’s frontier on the same hardware. Hardware is not magically getting more memory or bandwidth. Believing there will be some magical optimizations to compensate for it is just dellusion.
- chlorion 1mo ago
  Then explain how equal parameter size models can grow in capability every few months or year?
- srcreigh 1mo ago
  Open weight models have been getting better/smaller every year. Also, from what I can tell, MLX inference is not as well optimized as CUDA, and the M5 Ultra has additional kinds of AI compute which is unavailable on other M models. With the massive 1.2 TB/s 512GB Mac studios coming out, I think MLX will get a lot more attention. In short: Todays models should run faster next year, and next year's models should also be more efficient.

EagnaIonat 1mo ago

Depends on what you plan to do. You don't need frontier models to summarise or create an email.