> In modern AI clusters, the network is no longer just infrastructure sitting beneath compute
It always make me smile when someone is presenting these kind of topologies as "New", "Modern A.I" or anything remotely "Revolutionary".
The HPC domain and any decent supercomputers have been doing RDMA networking centered around "all-to-all" and "all-reduce" operations for at least 3 fucking decades now.
They are the main reasons supercomputers are almost always constructed around stupidly complex Torus or Dragonfly network topologies.
MPI itself has these primitives defined from v1.
The only difference now is that it switch from "This niche thing 3 nerds were using for weather simulations" to "this cool thing any hyperscaler NEED to have for *A.I*"
in this context, that someone is an AI bot that spat out words.
PS: Taking a look at our manifesto (https://almartis.xyz/) can help with more context.
It's premature to discuss network architecture until that basic question is answered.
So unfortunately I'm inclined to assume this is empty conjecture shat out by an LLM. Because who would write something up in this much detail rather than typing `import numpy as ...` and going to town?
I'll also note that the document has all the usual crank signs. Lots of grand visions, hypotheses, and expounding at an overly high level on how various things work with hardly anything concrete.
Don't get me wrong. I don't mind when some tech bros burn billions of venture capital & nothing much (?) comes out of it.
But those datacenters embody a lot of resources. Raw materials, complex/resource heavy manufacturing processes for IC's, servers, networking gear, etc etc.
I sure hope that doesn't go to waste when the AI bubble pops. Datacenter stuffed with AI optimized hardware any good for general engineering? Science projects? Weather prediction? Web hosting? ...??