Build and Train an LLM with JAX

2026-03-04 · Source: DeepLearningAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

A new course, "Build and Train an LM with Jax," developed in partnership with Google and artist Rashadant, teaches participants to construct and train a small 20 million parameter language model from scratch using JAX. JAX is a numerical computing library from Google, similar to NumPy, but optimized for large model training with features like automatic gradient computation and efficient distribution across CPUs, GPUs, and TPUs. Google developed JAX for its flexibility and high performance, enabling rapid iteration on model architectures and training on tens of thousands of chips. The course focuses on building a GPT-2 style LLM, covering architecture creation, data loading, model training, checkpoint saving, and interacting with a pre-trained model via a graphical interface. It also explores the broader JAX ecosystem for scaling LLM development.

Key takeaway

For AI Engineers and Machine Learning Engineers looking to build and train custom language models, this course offers a practical pathway using JAX. You will gain hands-on experience with a powerful Google-developed library, learning to construct model architectures, manage data, and scale training efficiently. This knowledge is directly applicable to developing and experimenting with LLMs, providing a solid foundation for working with advanced AI models.

Key insights

JAX provides a flexible, high-performance framework for building and training large language models efficiently.

Principles

• JAX automates gradient computation.
• JAX distributes compute across diverse hardware.
• Rapid iteration requires flexible systems.

Method

The course method involves building a GPT-2 style LLM architecture, preparing data with JAX tools, training the model, saving checkpoints, and interacting with a pre-trained version.

In practice

• Build a 20 million parameter LM.
• Utilize JAX for data loading.
• Chat with a mini GPT via GUI.

Topics

• JAX
• LLM Training
• GPT-2 Architecture
• Distributed Computing
• Machine Learning Models

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by DeepLearningAI.