Training large language models pushes the limits of current hardware. Storing the parameters, activations, and gradients for models with billions or trillions of parameters often exceeds the memory capacity of a single accelerator like a GPU or TPU. Similarly, the computational cost, especially the quadratic complexity $O(N^2)$ of self-attention with respect to sequence length $N$, makes training times prohibitively long on one device.

To overcome these limitations, we must distribute the training process across multiple computational devices. This chapter introduces the fundamental techniques for parallelizing LLM training.

You will learn about:

• Data Parallelism (DP): Replicating the model across devices and splitting the data batch.
• Tensor Parallelism (TP): Splitting individual model layers or tensors across devices (intra-layer parallelism).
• Pipeline Parallelism (PP): Partitioning the model layers into stages and assigning stages to different devices.
• Hybrid Approaches: Combining these strategies (e.g., DP + TP, DP + PP) for optimal performance.
• Communication Costs: Analyzing the communication overhead associated with different parallelism methods.

Understanding these strategies is essential for effectively training state-of-the-art language models. We will examine the mechanics, benefits, and trade-offs of each approach.