Minisforum MS-S1 is the same chip but has a PCIe slot suitable for a network card.

Presumably that's why the subheading is:

>First wave of Ryzen AI desktop CPUs targets business PCs rather than DIYers.

Yeah the next generation of Strix Halo is what would get me excited. I think right now TSMC has no capacity, so maybe we have to wait another year. Kinda ironic that all CPU/RAM capacity is being sold to LLM companies, and as a result we can't get the hardware needed for good local LLMs.

Putting Strix Halo into the AM5 socket would make no sense. Half the memory controllers would be orphaned and the GPU would be severely bandwidth-starved (assuming that the memory controller on Strix Halo actually supports DDR5 and not just LPDDR5).

That is already the case with datacenter "GPUs". A A100, MI300 or Intel PVC/Gaudi does not have useful graphics performance nor capabilities. Coprocessors ala NPU/VPU are also on the rise again for CPUs.

That’s already the norm no?

Pretty much every hardware vendor has an NPU

Yes.

Even the latest NVIDIA Blackwell GPUs are general purpose, albeit with negligible "graphics" capabilites. They can run fairly arbitrary C/C++ code with only some limitations, and the area of the chip dedicated to matrix products (the "tensor units") is relatively small: less than 20% of the area!

Conversely, the Google TPUs dedicate a large area of each chip to pure tensor ops, hence the name.

This is partly why Google's Gemini is 4x cheaper than OpenAI's GPT5 models to serve.

Jensen Huang has said in recent interviews that he stands by the decision to keep the NVIDIA GPUs more general purpose, because this makes them flexible and able to be adapted to future AI designs, not just the current architectures.

That may or may not pan out.

I strongly suspect that the winning chip architecture will have about 80% of its area dedicated to tensor units, very little onboard cache, and model weights streamed in from High Bandwidth Flash (HBF). This would be dramatically lower power and cost compared to the current hardware that's typically used.

Something to consider is that as the size of matrices scales up in a model, the compute needed to perform matrix multiplications goes up as the cube of their size, but the other miscellaneous operations such as softmax, relu, etc.. scale up linearly with the size of the vectors being multiplied.

Hence, as models scale into the trillions of parameters, the matrix multiplications ("tensor" ops) dominate everything else.

We are dealing with a hype, but the reality is that AI would change everything we do. Local models will start being helpful in [more] unobtrusive ways. Machines with decent local NPUs would be usable for longer before they feel too slow.

I'd actually love to have an NPU that isn't useless on my 285k.

Indeed, I was buying a laptop for my wife, and she was viscerally against "Ryzen AI": I don't want a CPU with builtin AI to spy on my screen all the time!

They can just buy a regular Ryzen 9000 series CPU, then. Maybe add a real graphics card if they're into gaming.

Narrator: The RAM prices did not, in fact, go down.