Hardware3h ago
Hardware
Scaling ML Systems to Trillion-Trillion FLOPs
A trillion trillion floating point operations now define the frontier of large-scale training. This scale demands a fundamental shift in how engineers manage memory and interconnects to prevent systemic bottlenecks. Practitioners must prioritize hardware-aware optimization over simple model scaling. Failure to align software with GPU clusters at this magnitude leads to catastrophic efficiency losses.