Visualising Tensor Parallelism: Row vs Column Matrix Multiplication

HowCanAIHelp · Beginner ·🧠 Large Language Models ·1mo ago

Skills: ML Maths Basics53%Maths for ML53%

About this lesson

In distributed training, large weight matrices are split across multiple GPUs to save memory and speed up computation. - Column Parallelisation: Splits the matrix column-wise, computing partial outputs locally before gathering them. - Row Parallelisation: Splits the matrix row-wise, dividing the inner dimension and reducing the final output across GPUs. Watch how the math aligns to make training massive Large Language Models (LLMs) possible! Drop a like if you want more deep learning visuals, and subscribe for deep dives 👇 Tags: #DeepLearning #MachineLearning #AI #TensorParallelism #GPUComputing #DistributedTraining #DataScience #TechShorts #LLM #Coding

Original Description

In distributed training, large weight matrices are split across multiple GPUs to save memory and speed up computation. - Column Parallelisation: Splits the matrix column-wise, computing partial outputs locally before gathering them. - Row Parallelisation: Splits the matrix row-wise, dividing the inner dimension and reducing the final output across GPUs. Watch how the math aligns to make training massive Large Language Models (LLMs) possible! Drop a like if you want more deep learning visuals, and subscribe for deep dives 👇 Tags: #DeepLearning #MachineLearning #AI #TensorParallelism #GPUComputing #DistributedTraining #DataScience #TechShorts #LLM #Coding

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