Distributing Massive AI Models With Network-Layer Multicast
When you are pushing terabytes of weights to hundreds of GPU nodes, unicast stops being a solution. Here is what actually works — and where multicast still struggles.
The Problem Engineers Hit at Scale
If you have ever watched a 70-billion-parameter model take 20 minutes to load across a 200-node inference cluster, you have felt this problem in practice. The culprit is almost always the same: the model server opens a separate TCP stream to each receiver, saturating its own NIC before the first node finishes loading.
This is not a configuration issue. It is the fundamental geometry of unicast in a one-to-many scenario. For every additional receiver you add, the sender's bandwidth demand grows linearly. Distribute a 1 TB model to 100 nodes, and you are generating roughly 100 TB of traffic — all of it originating from the same host, all of it transiting the same top-of-rack...
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