How large is the example model in this course?

The course demonstrates a small GPT-2 style LLM with around 20 million parameters, built from scratch using Jax. (start at 66s)

What are the main steps covered in the course?

You’ll define the model architecture, use data loading tools to prepare a dataset, train the model and save a checkpoint, and finally load a pre-trained model to chat via a graphical interface. (start at 66s)

Where can Jax scale to multi-device training?

Jax can compile and distribute compute over many CPUs, GPUs, or TPUs, enabling scalable training across thousands of chips. (start at 35s)

What happens after training the model?

After training, you can load a pre-trained model and interact with it through a graphical interface, effectively chatting with a mini GPT. (start at 82s)

What is the overall takeaway from this course?

The course teaches core concepts for building and training cutting-edge LLMs with Jax and demonstrates how to experiment and scale architectures. (start at 101s)

Build and Train an LLM with JAX

DeepLearning.AI

Education4 min read2 min video

Mar 4, 2026|4,292 views|127|9

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Key Moments

TL;DR

Build and train a 20M-parameter GPT-2 style LLM in JAX, then chat with it via a GUI.

Key Insights

JAX combines NumPy-like usability with automatic differentiation and XLA-based speed, enabling efficient LM training.

The ecosystem supports compiling and distributing compute across CPUs, GPUs, and TPUs for scalable model training.

A small GPT-2 style LLM (about 20 million parameters) can be built from scratch, illustrating core architecture and training concepts.

Data preparation (e.g., storytelling datasets) and robust checkpointing are essential components of practical LM pipelines.

Training from scratch and then loading a pre-trained model for interactive chat demonstrates end-to-end ML workflow.

Google’s real-world models (e.g., Nano Banana, Vio, Gemini) illustrate JAX’s role in scalable, production-grade LLMs.

INTRODUCTION AND CONTEXT: WHY JAX FOR LLMs

This overview establishes the course goal of building and training a small LLM using JAX in collaboration with Google and Rashadant. The narrative frames JAX as the backbone for modern LM development, leveraging a 20 million parameter GPT-2 style model built from scratch. The transcript notes that JAX underpins Google’s open-source tooling and powers famous models like Nano Banana, Vio, and Gemini, illustrating its industry relevance. The project emphasizes rapid iteration on model architectures, high performance, and the ability to train across vast hardware resources. Learners will progress from architecture design to data preparation, training, checkpointing, and finally interactive chat via a graphical interface, providing a complete end-to-end workflow. Entering this course, one gains exposure to JAX’s ecosystem and the practical steps needed to experiment with cutting-edge LM concepts at scale.

FROM NUMPY TO JAX: FLEXIBILITY, GRADIENTS, AND DISTRIBUTED COMPUTATION

The discussion contrasts JAX with NumPy, highlighting JAX’s additional capabilities that are critical for LM training. JAX offers automatic gradient computation, enabling efficient backpropagation, and a compiler-enabled path that accelerates code execution. The platform is designed for distribution across many CPUs, GPUs, or TPUs, which makes it suitable for scaling training to large models and large datasets. The transcript connects these features to real-world practice, noting that large Google models are built with JAX, and that the library supports quick experimentation with model architectures while scaling up as needed. For learners, this section clarifies why JAX is a natural fit for building and training LLMs efficiently across diverse hardware.

BUILDING A SMALL GPT-2-STYLE LLM: ARCHITECTURE AND PARAM COUNTS

The core project centers on constructing a GPT-2 style, decoder-only transformer with approximately 20 million parameters. This design choice provides a manageable yet representative platform to explore the essential building blocks of modern LLMs: token embeddings, transformer blocks, attention mechanisms, and language modeling objectives. The course outlines how to translate these ideas into code within JAX, emphasizing architecture decisions that support learnable representations and effective context handling at a modest scale. Learners will gain hands-on experience outlining the model, configuring hyperparameters, and aligning the architecture with the dataset and training objectives to illustrate the practicalities of creating a functional LM from scratch.

DATA LOADING, TRAINING, AND CHECKPOINTING: PRACTICAL STEPS

A major focus is the end-to-end training pipeline, starting with data preparation. Learners will use JAX’s data loading tools to assemble a dataset consisting of many stories, creating a diverse training corpus to teach the model language patterns. The training loop covers core elements: forward pass, loss computation, backpropagation, and parameter updates, all optimized for performance on modern accelerators. Checkpointing is emphasized to preserve progress and enable resumption or experimentation. The transcript stresses that JAX’s ecosystem supports efficient data handling, minimized training bottlenecks, and robust state saving, which are essential for iterative LM development at scale.

INTERACTIVE CHAT AND GUI: FROM TRAINED MODEL TO REAL-TIME INTERACTION

Following training, the workflow includes loading a pre-trained model and enabling interactive chat via a graphical interface. This part demonstrates how the trained parameters translate into usable inference, including a simple user-facing chat experience. The GUI serves as a practical tool for evaluating model behavior, response quality, and real-time responsiveness, providing a hands-on way to observe the LM’s capabilities after training. It also highlights how checkpoint restoration and model loading are integral to transitioning from offline training to real-time interaction.

ECOSYSTEM, SCALABILITY, AND WHY THE COURSE MATTERS

The final section broadens the view to JAX’s broader ecosystem, which includes libraries and tooling designed to accelerate LM development across thousands of TPUs or GPUs. Learners are shown how to leverage these tools to experiment with different architectures quickly and to scale training as needed. The course underscores practical takeaways: JAX supports rapid iteration, flexible architectures, robust distributed training, and a path toward production-grade LM workflows. By engaging with the material, participants gain a solid foundation in the core concepts underpinning modern LM construction and deployment, as well as a concrete, end-to-end experience from building a small model to interactive usage.

Mentioned in This Episode

●Tools & Products

●People Referenced

Jax LLM Training Cheat Sheet

Practical takeaways from this episode

Do This

Start with a small model (e.g., GPT-2 style) to validate the training pipeline.

Use Jax's data loading tools to prepare a dataset before training.

Avoid This

Skip data preparation or skip saving checkpoints.

Assume the model will scale across devices without testing first.

Common Questions

Jax is a numerical computing library that enables fast, gradient-based computation across CPUs, GPUs, and TPUs. The video presents it as central to building and training large language models, thanks to automatic differentiation and scalable distribution. (start at 14s)