The scale of large language models has grown exponentially in the past two years, from GPT-3's 175 billion parameters to trillion parameter level models, and the computational resources required for training have far exceeded the limit of a single machine. How to efficiently utilize distributed computing power for large-scale model training has become a core challenge in the field of AI infrastructure.
Data parallelism is the earliest widely adopted distributed training strategy. The core idea is to divide the training data into multiple small batches, distribute them to different GPUs to calculate gradients simultaneously, and then summarize the gradients through the AllReduce communication protocol and synchronously update the model parameters. The advantage of this strategy is that it is easy to implement and has almost no intrusion into the model code. However, when the model size exceeds billions of parameters, each GPU needs to store a complete copy of the model, and the bottleneck of video memory begins to emerge. Taking the GPT-3 with 175B parameters as an example, only the model parameters require about 350GB of FP16 memory, far exceeding the capacity of a single A100 with 80GB.
Model Parallelism has emerged. It splits different layers or parameters of the model onto multiple GPUs, with each GPU only responsible for computing a portion of the model. Tensor parallelism divides the parameters within a single Transformer layer into multiple devices, and each device collaborates to perform forward and backward calculations for one layer. Pipeline parallelism allocates different layers of the model to different devices and performs training in a pipeline manner - device 1 calculates layers 1-8 and then passes it on to device 2 to calculate layers 9-16, and so on. This design makes it possible to train trillion parameter models.
In recent years, ZeRO optimizer and mixed precision training framework have further promoted the improvement of training efficiency. ZeRO eliminates redundant storage by distributing optimizer state, gradients, and model parameters across all GPUs, doubling the size of trainable models under the same hardware conditions. Combined with BF16 mixed precision training, the throughput of single card computing has been increased by 2-3 times, while maintaining the convergence accuracy of the model.
In 2026, large-scale training infrastructure is evolving towards Wanka clusters. DeepSpeed, Megatron LM and other technology stacks support flexible combinations of 3D parallelism (data parallelism+tensor parallelism+pipeline parallelism), allowing users to automatically select the optimal parallel strategy based on model size and hardware topology. In the future, heterogeneous computing (GPU+NPU+TPU hybrid cluster) and intelligent resource scheduling will become the next frontier direction for computing power optimization. Every time the computational efficiency of training large models doubles, it means that larger and more intelligent models can be trained under the same hardware conditions, which will be the fundamental guarantee for the continuous breakthrough of AI technology.