How to Build and Train an LLM with JAX in 2026: Google’s Gemini Framework Guide

How to Build and Train an LLM with JAX in 2026: Google’s Gemini Framework Guide

Building and training an LLM with JAX is now more accessible than ever, thanks to a new short course launched by DeepLearning.AI in partnership with Google. JAX, the open-source numerical computing library at the heart of Google’s most advanced AI models—including Gemini—offers developers a powerful blend of NumPy-like syntax, automatic differentiation, just-in-time compilation, and seamless scaling across thousands of CPUs and GPUs. This course, taught by Chris Achard, Developer Relations Engineer on Google’s TPU Software team, demystifies the infrastructure that powers cutting-edge large language models.

Why JAX Is the Foundation of Google’s AI Stack

JAX distinguishes itself from traditional frameworks by combining the simplicity of NumPy with advanced features critical for modern AI research. Its automatic differentiation system allows for precise gradient computation without manual derivation, while XLA-based just-in-time compilation accelerates execution on TPUs and GPUs. These capabilities make JAX ideal for iterative model development, where rapid experimentation and high-throughput training are essential.

Step-by-Step LLM Training with JAX

According to DeepLearning.AI, the course walks learners through constructing a transformer-based LLM from scratch, implementing attention mechanisms, and optimizing training loops using JAX’s functional programming paradigm. Unlike black-box frameworks, JAX empowers developers to understand and control every layer of the computation graph, fostering deeper expertise in AI system design.

How JAX Enables TPU Scaling for Large Models

JAX’s native integration with Google’s Tensor Processing Units (TPUs) allows for distributed training at scale—something PyTorch and TensorFlow still struggle to match natively. With just a few code changes, models can be deployed across hundreds of TPU cores, enabling training of billion-parameter LLMs without expensive hardware investments.

JAX vs PyTorch: Which Is Better for Research?

While PyTorch excels in prototyping, JAX shines in production-grade scalability. Its functional programming model eliminates stateful side effects, making models more reproducible and easier to optimize. For researchers aiming to push the boundaries of LLM training, JAX offers finer control over memory, gradients, and hardware utilization.

Practical Benefits for Developers

Enrollment in the course is open to developers with intermediate Python skills and basic familiarity with machine learning. No prior experience with JAX is required, making it an ideal entry point for those looking to transition from PyTorch or TensorFlow to Google’s preferred research stack. The curriculum includes practical exercises using Google’s TPU cloud resources, ensuring learners gain real-world experience with production-scale training environments.

As AI models grow in complexity and size, the ability to efficiently train and fine-tune them becomes a competitive advantage. By learning to build and train an LLM with JAX, developers gain direct access to the same tools used by Google’s top AI labs. This democratization of high-performance AI infrastructure could accelerate innovation across academia, startups, and enterprise research teams.