This is commonly cited, but not strictly true.

Prompt processing is completely compute limited. And at high batch sizes, where the weights are read once for many tokens generated in parallel, token generation is also quite compute limited. Obviously you want enough bandwidth to match the compute, but its very compute heavy.

You can see this for yourself. Try ~10 prompts in parallel on a CPU in llama.cpp, and it will slow to a crawl, while a GPU with a narrow bus won't slow down much.

Training is a bit more complicated, but that's not doable on CPUs anyway.

Now, local inference (aka a batch size of 1), past prompt processing, is heavily bandwidth limited. This is why hybrid inference works alright on CPUs. But this doesn't really apply to servers, which process many users in parallel with each "pass".

…I mean, yeah? It’s obviously developed for the Chinese market.

But that’s theoretical, for now. No CPU backend I can find supports DSV4, and DeepSeek hasn’t contributed anything yet.

You've proved my point that you don't know what you're talking about by blindly linking to the git repo. Couldn't find any source that supports your claim? I wonder why.

Sure you can serve one request at a time to one patient user at a slow token per second rate, which makes running locally viable, but there is no RAM that has the bandwidth to run this model at scale. Even flash would be incredibly slow on CPU with multiple requests. You'd need the high bandwidth of VRAM and to run across multiple GPUs in a scalable way, it requires extremely high bandwidth interconnects between GPUs.