Training neural networks at the scale of billions of parameters presents unique challenges that go far beyond simply adding more GPUs. At Nexora, we've spent the past two years optimizing our infrastructure to handle models 100x larger than what we started with — and along the way, we've learned some lessons that surprised even our most experienced engineers.
The Memory Wall
The first and most obvious challenge is memory. A model with 100 billion parameters, stored in 16-bit precision, requires 200GB just for the weights. Add optimizer states, gradients, and activations, and you're looking at terabytes of memory per training step. Traditional data parallelism simply doesn't work at this scale.
"We had to rethink every assumption we had about distributed training. The techniques that worked for 1B parameters completely broke down at 100B."
Pipeline Parallelism Done Right
Our solution was a hybrid approach combining tensor parallelism within nodes and pipeline parallelism across nodes. But pipeline parallelism introduces its own challenges: the infamous "bubble" where GPUs sit idle waiting for forward and backward passes to complete.
We implemented interleaved pipeline scheduling, which splits each layer into multiple micro-batches and processes them in a round-robin fashion. This reduced the bubble overhead from 50% to under 15%.
Communication Optimization
At this scale, communication between GPUs becomes the bottleneck. We invested heavily in custom all-reduce implementations that take advantage of the specific topology of our GPU clusters. By carefully mapping tensor shards to physical hardware, we reduced all-reduce time by 40%.
The result? We can now train a 100B parameter model in roughly the same time it previously took us to train a 10B model — a 10x improvement in effective training throughput.