What Is Model Parallelism? Splitting the Model Itself Across Machines
When a model is too large to fit on a single machine, model parallelism splits the model itself across multiple machines instead of just the data.
Splitting the model, not just the data
Model parallelism splits the model's own parameters across multiple machines — each machine holds and computes only a portion of the model — rather than giving every machine a complete copy the way data parallelism does.
Why this is necessary for the largest models
Frontier models can have parameter counts far exceeding what a single machine's memory can hold, no matter how the training data gets split. Model parallelism is what makes training such models possible at all, by distributing the model's own memory footprint across many machines instead of trying to fit it on one.
The real cost: heavy communication between machines
Because different parts of the same model now live on different machines, they constantly need to exchange intermediate results with each other during both the forward and backward pass, which requires very fast networking between machines — communication overhead is the central engineering challenge in model parallelism.
Frequently Asked Questions
What is model parallelism?
A distributed training strategy that splits a model's own parameters across multiple machines, with each machine holding and computing only a portion of the model, rather than every machine holding a full copy.
Why is model parallelism necessary for the largest AI models?
Frontier models can have parameter counts exceeding what a single machine's memory can hold. Model parallelism distributes the model's memory footprint across many machines, making training such large models possible at all.