Pipeline Parallelism

Pipeline parallelism is a distributed training technique that partitions a neural network's layers across multiple GPUs or nodes, with each device handling a contiguous slice of the model. During training, micro-batches flow through the pipeline like an assembly line — device 1 processes layers 1-8 of micro-batch 1, then passes activations to device 2 (layers 9-16) while starting work on micro-batch 2. The technique enables training of models too large to fit on any single device, complementing data parallelism (replicates the model) and tensor parallelism (splits within a layer). Pipeline parallelism's main challenge is the "bubble" — idle GPU time at the start and end of each batch when the pipeline is not yet full or draining — which is mitigated by techniques like interleaved 1F1B scheduling. Frameworks supporting pipeline parallelism include DeepSpeed, Megatron-LM, PyTorch (via torch.distributed.pipelining), and Colossal-AI. AI governance teams encounter pipeline parallelism mainly in training infrastructure documentation for self-hosted LLM training. The technique is essential at frontier scale, with GPT-4-class training combining pipeline, tensor, and data parallelism in 3D arrangements across thousands of GPUs.

Pipeline-trained models through Centralpoint: Centralpoint operates above whatever distributed training topology produced your models, with consistent metering across the LLM stack. The model-agnostic platform routes to any LLM, keeps prompts local, supports generative and embedded models, and deploys chatbots through one line of JavaScript with audit-ready governance.


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