What Is Distributed Training? Training a Model Across Many Machines at Once
A frontier model is far too large and slow to train on a single machine — distributed training spreads the work across thousands of machines working together.
Spreading the work across many machines
Distributed training spreads the computational work of training a model across many machines — often thousands of GPUs — working together in a coordinated way, rather than the entire training process running on a single machine.
Why a single machine simply isn't enough
A frontier model's parameters and training data are far too large to fit in a single machine's memory, and even if they did fit, training would take an impractically long time running on just one machine's compute — distribution is a genuine necessity, not an optimization applied on top of an already-feasible approach.
The main approaches to splitting the work
Distributed training typically combines several strategies: data parallelism (splitting the data across machines), model parallelism (splitting the model itself), and pipeline parallelism (splitting the model's layers into stages) — real training runs often combine more than one of these together.
Frequently Asked Questions
What is distributed training?
Spreading the computational work of training a model across many machines working together in a coordinated way, rather than running the entire process on a single machine.
Why is distributed training necessary for large models?
A frontier model's parameters and training data are too large to fit on a single machine, and training on just one machine's compute would take an impractically long time — distribution is a genuine necessity, not an optional optimization.