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MaxText is a high-performance, highly scalable open-source framework designed to train and fine-tune large language models using the JAX ecosystem. The project acts as both a reference implementation and a practical training library that demonstrates best practices for building and scaling transformer-based language models on modern accelerator hardware. It is optimized to run efficiently on Google Cloud TPUs and GPUs, enabling researchers and engineers to train models ranging from small experiments to extremely large distributed workloads. The framework focuses on simplicity while still supporting advanced techniques such as model sharding, distributed computation, and high-throughput training pipelines. MaxText includes ready-to-use configurations and reproducible training examples that help developers understand how to deploy large-scale AI workloads with modern machine learning infrastructure.
Features
- High-performance distributed LLM training with JAX and XLA
- Native support for Google Cloud TPU and GPU infrastructure
- Reference implementations of transformer-based language models
- Tools for pre-training, fine-tuning, and reinforcement learning post-training
- Efficient scaling from single-host experiments to large clusters
- Integrated logging, monitoring, and reproducible training pipelines
Project Samples
