Scaling Large Language Models: Getting Started with Large-Scale Parallel Training of LLMs

Scaling Large Language Models: Getting Started with Large-Scale Parallel Training of LLMs

by Shashank Shekhar

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQMMachine learning
🎙 Shashank Shekhar 👥 5K 📅 September 28, 2025 ⏱ 79 min 👁 237 📄 tutorial 🧭 2026-08-15
Available in: English (current) Français

Keywords

data parallelismtensor parallelismpipeline parallelismall-gatherall-reduce

Summary

This workshop, led by Shashank Shekhar, provides a hands-on introduction to scaling large language models through parallel training. The session focuses on fundamental parallelism dimensions—data, tensor, and pipeline parallelism—and how to compose them for training billion-parameter models. Using JAX and TPUs in Google Colab, participants implement each strategy from first principles, understanding trade-offs and optimizing communication patterns. The tutorial covers matrix distribution across devices, sharding strategies, and collective communication operations like all-gather and all-reduce. It emphasizes the importance of communication versus computation in distributed training. The speaker explains how to create a device mesh, partition data, and handle matrix multiplications with different sharding configurations. The session includes live coding and practical exercises, allowing participants to implement parallelization techniques directly. The workshop is designed for practitioners familiar with deep learning but new to distributed training, providing a solid foundation for scaling models efficiently.

142 words

Critical Evaluation

Value of the Information & Strength of the Argument

The workshop provides high practical value by teaching parallelization from first principles, avoiding high-level frameworks to expose underlying mechanics. The argumentation is clear and logical, building from basic sharding concepts to collective operations and their costs. The speaker effectively uses visualizations and code examples to illustrate complex ideas, making the material accessible. The emphasis on communication overhead and trade-offs is valuable for practitioners. The session is well-structured, with each concept building on the previous, and includes interactive elements to reinforce learning.

Scientific Rigor, Source Quality, Title Accuracy

The scientific rigor is high, with accurate explanations of parallelism and collective operations. The speaker references a book ‘How to Scale Your Model’ and provides links in the notebook, though these are not explicitly cited in the talk. The title accurately reflects the content, which is a practical introduction to large-scale parallel training. The workshop is based on the speaker’s expertise and experience, lending credibility. However, the lack of formal citations within the talk limits verifiability. The content is technically sound and aligns with established practices in distributed training.

185 words

Title / Content Match

The title accurately reflects the content, which is a practical introduction to large-scale parallel training of LLMs.

Quality & Reliability

8/10

The workshop is presented by an independent researcher with a strong background (Meta AI, Vector Institute, NeurIPS best paper) and provides hands-on, first-principles explanations of parallelization techniques. The content is technically accurate and well-structured, though it relies on the speaker's expertise and lacks formal citations within the talk.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

  • How to Scale Your Model — Referenced in the notebook as a resource for visualizations of sharding strategies.

Concurring Sources

  • How to Scale Your Model — Referenced as a resource for sharding visualizations.

Contribution & Novelties

The workshop provides a hands-on, first-principles approach to parallel training, which is valuable for practitioners. It demystifies the underlying mechanics of data, tensor, and pipeline parallelism, and emphasizes communication overhead. The use of JAX and TPUs makes it accessible for free. The session also touches on recent techniques like context parallelism, though not in depth.

Pour aller plus loin :

  • Data parallelism — Overview of data parallelism, a key concept covered.
  • Tensor parallelism — Explanation of tensor parallelism, another core topic.
  • Pipeline parallelism — General concept of pipelining, applicable to pipeline parallelism.
  • All-reduce — Detailed description of the all-reduce collective operation.
  • JAX documentation — Official documentation for JAX, the framework used in the workshop.

114 words

Radar Profile

The radar profile shows high scores across all dimensions, indicating a well-rounded and reliable tutorial. The strong technical depth and practical focus make it particularly valuable for practitioners seeking to understand parallel training.

Reliability 8/10