LLM inference, most likely

I don't see a price there, so I'm not sure how "overpriced" it is. But it's a class of product that is expensive across the board, compared to a typical desktop workstation or gaming PC.

The use cases are mostly local AI. That's very demanding and requires compromises one way or another. You want high memory capacity and high memory bandwidth most of all. You can't get both without paying out the nose, so you need to find the sweet spot for your specific use cases.

Discrete GPUs give you high bandwidth, but low capacity.

Standard desktop memory gives you high capacity, but low bandwidth (and also low compute speed since CPUs are no good for the workloads).

This type of product is a middle ground. Integrated memory gives you better bandwidth than DDR5, but not as good as a discrete GPU. Integrated GPU/NPU gives you much better performance than a desktop CPU, and can access the full integrated memory directly. Upgradeable memory is not really possible for this class of product.

The real competition for this is a tricked-out MacBook Pro. Those are faster, but they ain't cheap, and I don't think Apple even sells any 192GB Macs anymore, and when they did it was close to $10k.

The reason (besides ram apocalypse) for the price and non upgradability is the ram is very fast low latency unified memory. It's close to using VRAM for the CPU and gpu.

There's a standard for upgradable lpcamm2 but Framework didn't use it probably because it's even more impossible to source with the RAM shortage.

It's a small box that supposedly does some specific workloads well. It's a weird product for framework though.

It is targeting the local AI inference market. This is a good choice for a developer who wants one x86 machine to run models that cross the threshold that fits comfortably into 128GB of unified RAM, which is quite a few.

I was trying to work out the same thing. The whole idea of Framework is a repairable and modular product in a market where most products are entirely the opposite.