pipeline parallelism

Architect

May 26, 2025 · Artificial Intelligence

Parallelism Strategies for Large-Scale Model Training: Data, Tensor, Pipeline, Sequence, and Expert Parallelism

This article explains the memory limits of a single GPU and systematically introduces data parallelism, tensor parallelism, pipeline parallelism, sequence parallelism, and expert parallelism, describing their communication costs, advantages, drawbacks, and practical implementation details for training large AI models.

AI trainingData ParallelismLarge Language Models

0 likes · 14 min read

Parallelism Strategies for Large-Scale Model Training: Data, Tensor, Pipeline, Sequence, and Expert Parallelism

DataFunTalk

Jul 8, 2024 · Artificial Intelligence

Challenges and Techniques for Distributed Training of Large Language Models

This article discusses the historical background, major challenges such as massive compute and memory demands, and the technical ecosystem—including data parallelism, pipeline parallelism, and optimization strategies like DeepSpeed and 1F1B—to enable efficient distributed training of large language models.

AI infrastructureDeepSpeedLarge Language Models

0 likes · 22 min read

Challenges and Techniques for Distributed Training of Large Language Models

DataFunSummit

Mar 31, 2024 · Artificial Intelligence

Challenges and Techniques in Distributed Training of Large Language Models

This article reviews the rapid development of large language models since 2019, outlines the historical background, identifies key challenges such as massive compute demand, memory constraints, and system complexity, and then details distributed training technologies—including data parallelism, pipeline parallelism, and advanced optimization strategies—while also discussing future research directions and answering common questions.

AI infrastructureData ParallelismDeepSpeed

0 likes · 23 min read

Challenges and Techniques in Distributed Training of Large Language Models

Alimama Tech

Sep 12, 2023 · Artificial Intelligence

Megatron-LLaMA: High-Performance Large Language Model Training Framework

Megatron-LLaMA is an open‑source high‑performance training framework for LLaMA models, offering tensor, pipeline, and sequence parallelism, an overlapped optimizer, and near‑linear scalability, achieving up to 176% speedup on 32 GPUs and robust performance even with limited network bandwidth.

DeepSpeedGPU optimizationLlama

0 likes · 10 min read

Megatron-LLaMA: High-Performance Large Language Model Training Framework

AntTech

May 24, 2022 · Artificial Intelligence

WPipe: Group‑Based Interleaved Pipeline Parallelism for Large‑Scale DNN Training

The paper introduces WPipe, a group‑based interleaved pipeline parallelism method that reduces memory overhead and weight‑update latency compared with PipeDream‑2BW, achieving up to 1.4× speed‑up and 36% lower memory usage while preserving model accuracy on large‑scale DNNs.

Memory EfficiencyWPipedeep learning

0 likes · 13 min read

WPipe: Group‑Based Interleaved Pipeline Parallelism for Large‑Scale DNN Training

DataFunSummit

Apr 25, 2022 · Artificial Intelligence

Token‑Level Pipeline Parallelism for Transformer‑based Language Models (TeraPipe)

The article introduces a token‑level pipeline parallelism strategy that splits the sequence‑length dimension of Transformer‑based language models, explains why this approach is feasible, presents a dynamic‑programming formulation for optimal slicing, discusses engineering challenges, and evaluates its performance on large GPT models.

Large Language ModelsToken-levelTransformer

0 likes · 13 min read

Token‑Level Pipeline Parallelism for Transformer‑based Language Models (TeraPipe)