Reference 4 stops to get here

Tensor Parallelism

Splitting individual layers/tensors across devices for very large models.

Your route here

4 stops · basics first
  1. GPU ✓ understood

    Graphics Processing Unit - hardware accelerator with thousands of cores, essential for parallel computation in deep learning.

  2. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  3. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  4. Data Parallelism ✓ understood

    Replicating the model across devices, each processing different data batches.

  5. Tensor Parallelism · you are here ✓ understood

Splitting individual layers/tensors across devices for very large models.

This concept is essential for understanding training & optimization and forms a key part of modern AI systems.

  • Model Parallelism
  • Distributed Training
  • Large Models

Where it sits

Before this

Data Parallelism
Tensor Parallelism

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Training Pipeline Parallelism Splitting model layers across devices and processing micro-batches in pipeline fashion. Training ZeRO Zero Redundancy Optimizer - techniques for memory-efficient distributed training by partitioning optimizer states. Training Sharded Data Parallelism Distributing model states across devices to train models larger than single-device memory. Neural Networks Mixture of Experts An architecture where multiple specialized sub-networks (experts) process inputs, with a gating network routing to relevant experts.