Techniques for training large neural networks

Training a massive neural network isn't just about having enough GPUs sitting in a rack. It's about getting hundreds or thousands of them to act like one giant brain doing a single synchronized calculation, without wasting half their time waiting on each other. OpenAI's rundown of the field makes clear that this coordination problem, not raw compute, is often the real bottleneck.

The simplest approach, data parallelism, just copies the whole model onto every GPU and feeds each copy a different slice of the batch, syncing gradients afterward. That works fine until the model itself is too big to fit on a single chip, which is exactly the situation with today's largest systems. At that point you need to slice the model itself apart.

That's where pipeline parallelism and tensor parallelism come in. Pipeline parallelism splits the network by layer, so different GPUs handle different stages of the same forward pass, like an assembly line. Tensor parallelism goes further, cutting individual layers into pieces so multiple GPUs share the math for a single operation. Both approaches introduce their own headaches, mostly around GPUs sitting idle waiting for data from a neighbor, which is why real systems usually blend several of these methods at once rather than picking just one.

Then there's mixture-of-experts, a design where the network learns to route each input to only a handful of specialized subnetworks instead of running everything through the entire model. It's a clever way to grow parameter count without proportionally increasing compute cost per token, though it complicates load balancing since some experts inevitably get called on more than others.

None of this is glamorous work. It's memory bandwidth, network topology, and scheduling — the plumbing behind the flashy demos. But as models keep scaling, the difference between a lab that can train efficiently and one that can't increasingly comes down to exactly these unsexy engineering choices.