[Catloafdev]

If you want frontier-level, the economically reasonable option is OpenRouter or a direct sub to frontier-of-your-choice.

The reality is that they do not offer configurations that would allow a consumer to run that much VRAM on a single setup to protect datacenter margins. Apple used to, and they stopped, those devices are going for ~$20k+ each on ebay now.

You can get very, very capable models on a 3090/4090/5090/6000 series card. But if you want 'frontier level' you are investing ~22k at a bare minimum if you go new. Used you can probably build your own server for much cheaper up-front cost but it's likely going to be 4-6x+ electricity usage.

[theossuary]

I truly think by 2028 we'll have integrated chip systems that'll be able to run opus 4.8 level models at ~500 watts at acceptable performance. Honestly I think now is the worst time to invest in AI hardware. Get your harness ready and processes perfected with hosted models, and wait a few years to buy hardware to transition to running models locally

[baq]

Burning weights onto a chip in an efficient way and exposing that via USB would be acceptable for a good enough model tbh

[ajbourg]

This is pretty close to what Taalas is doing.

[calgoo]

Trying Taalas is almost scary, there is something unsettling with that speed! Even with that small model, because of the speed, you could run hundreds of sample runs in a second, and pick from the best.

Can't wait for their next release!

[AlexCoventry]

Right now, we seem to be shambling toward a war which would hit globalized industrial processes very hard. Buying decent hardware now might wind up looking like good insurance against that.

[CamperBob2]

Honestly I think now is the worst time to invest in AI hardware.

That position is not without its own risks, though. Maybe Opus 4.8 will run on a single chip by 2028... and maybe you won't be allowed to touch it.

And what if Xi makes a play for Taiwan? That would be stupid, but so was invading Ukraine with tanks from Temu, and it still happened.

[dudisubekti]

Other than Taiwan declaring independence, I don't see any reason why China will rush to take the island.

At the very least they would wait until they cracked EUV and mass-produce the chips, and that is still 4-5 years away at the earliest.

[CptFribble]

> so was invading Ukraine

the difference is that Putin's hand was forced by age, (possibly) illness, and the last several decades of how he chose to run his country. Putin's power base is a relatively small group of elites and oligarchs who would happily snuff out the man who pushes them out of windows if they get too uppity, if they were given the chance. He needed the cover of war to maintain the fiction of his type of strongman "only I can save us" leadership.

Xi's power base is the simple fact that his leadership has transformed China into the #2, and now because of Trump possibly soon the #1 world superpower. He has also acted aggressively in the last decade to find and remove corruption and prevent individuals from accumulating the kind of wealth and influence that could threaten his power from outside official Party channels. Of course, as I'm not Chinese myself, I have no clue what the internals of Party politics actually look like. But as an outside observer it seems clear that Xi et. al. do not actually need Taiwan for anything other than national pride. They know the US would go to the mat to protect it as TSMC is extremely vital to US military power. And since China cannot compete in that arena and has too much to lose, they instead have focused on weakening the US from within, quite successfully of late.

By the time China finally takes Taiwan it will be with little fanfare and little consequence - they won't touch it until the US either has lost its military capabilities, or the US has its own internal chip industry. Anything else is an existential risk for the coastal cities that are China's entire economic advantage.

[hurtigioll]

if such hardware becomes available, it will be bought by the data-centers, just like they buy all the RAM today

[daemonologist]

There are also significant economies of scale (namely: utilization and batching), which tend to make inference on a shared server more economical even after the operator takes a cut.

[zozbot234]

You can use batching on consumer hardware, it just requires a KV-cache efficient model (or short context only) and keeping multiple inference flows running in parallel. This is most useful in combination with streamed inference, since the compute intensity of decode with those newer KV-compressed models is high enough that you have limited compute headroom when running at the speed of RAM.