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It seems relevant for playing with LLMs, but for actual work this seems far off for me.

My productivity profits from the best intelligence available, a decent context size, and a batch size of four.

While my MacBook has 48 GB of RAM, not only do I want the above requirements at a decent speed, but I also need my machine to run the development tools and test suites, ideally without the fans blasting at full load.

For the foreseeable future I will stay with providers rather than local inference, apart from niche use cases.

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Yeah, agree, but that's the point, really. If I could buy a 16Tb machine with 4 TPUs for ~$5K and run a frontier model locally, I would.

I'm in Australia, so we're probably not getting access to Fable again. We're learning that a faster model + better harness/framework > smarter model. So being able to run GLM5.2 locally and super-fast would be great.

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my only concern if the same specs today would cost 10x more given the trajectory of the growth of memory prices lately.
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I think this is where the new technology comes in. There is demand for 10x (or 1000x) the memory that we're using at the moment, so someone/something will satisfy that demand. We haven't had that demand up until now, because 16Gb was a perfectly reasonable amount of memory that could run pretty much anything, and if that won't then 32Gb will. There was zero demand for 16Tb memory machines because no-one had any application for that much memory. Now that's changing, and there is demand for that much, so we'd expect to see that being made available.

But the existing tech we're using for 16Gb probably isn't going to scale to 16Tb at a reasonable price point. And the price point is relatively inelastic - people are used to paying <$5K for their computers, and they're not going to go much above that. You'll get early adopters paying $10K or more for a machine that large, but not the early majority. And even then, obviously, $10K is not going to buy you a 16Tb memory machine.

So there's room for a new technology to come in, where there wasn't previously. This is what happened all through the 90's, and we churned through a bunch of standards and technologies to try and keep up with demand.

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