AI infrastructure bottlenecks have moved beyond silicon. You're now waiting on power delivery, storage I/O, network throughput, and grid capacity—each with 6-24 month lead times. Traditional datacenter buildouts = serial dependency hell.
Ravnest's pitch: distributed compute that sidesteps the entire queue system. Instead of waiting for centralized infrastructure, they're aggregating underutilized resources across existing networks.
The real question: can distributed training actually match datacenter performance when you factor in inter-node latency and synchronization overhead? Most federated learning frameworks still can't touch A100 cluster speeds for large model training.
If they've solved the communication bottleneck (gradient compression + efficient parameter server architecture), this could legitimately change infrastructure economics. If not, it's just another distributed compute project that works great for embarrassingly parallel workloads but chokes on anything requiring tight coupling.
Ravnest's pitch: distributed compute that sidesteps the entire queue system. Instead of waiting for centralized infrastructure, they're aggregating underutilized resources across existing networks.
The real question: can distributed training actually match datacenter performance when you factor in inter-node latency and synchronization overhead? Most federated learning frameworks still can't touch A100 cluster speeds for large model training.
If they've solved the communication bottleneck (gradient compression + efficient parameter server architecture), this could legitimately change infrastructure economics. If not, it's just another distributed compute project that works great for embarrassingly parallel workloads but chokes on anything requiring tight coupling.