jax

Google Cloud and RadixArk are partnering to bring the SGLang open-source inference framework to Google Cloud TPUs, initially via SGL-JAX and later with SGL-torchtpu for PyTorch-native support, enabling developers to run production workloads seamlessly across GPUs and TPUs.

An open-source tool called XProf Cubism Reducer reduces TensorBoard trace sizes by over 90% for JAX/XLA, making performance profiling more efficient.

JAXBench is a new benchmark suite of 50 JAX workloads for evaluating AI-generated kernel optimization on Google Cloud TPUs, with hand-tuned baselines and an agent evaluation harness. The paper finds that conditioning on curated TPU documentation significantly improves correctness and speedup, with Autocomp beam-search achieving up to 1.6x geomean speedup over XLA on hand-tuned kernels.

Google announces a major update to Tunix, its post-training library, with an asynchronous decoupled rollout engine to scale agentic reinforcement learning on JAX/TPU, eliminating idle time and improving throughput.

An experiment in formalized textbooks that implements Chapter 1 of Structure and Interpretation of Classical Mechanics in TypeScript and Jax, with code-math alignment and a simulator.

Google Cloud details how they optimized Qwen 3.5-397B MoE on Ironwood TPUs using a modular, model-agnostic engineering playbook, achieving 3.1× decode and 4.7× prefill performance gains.

Google launched the TPU Developer Hub, a centralized resource with documentation and framework recipes for building, training, and serving AI on Google Cloud TPUs, supporting JAX, PyTorch, and vLLM.

This blog post explores a technique to make legacy Fortran simulation code differentiable using LFortran, Enzyme, and Tesseract, allowing automatic differentiation and integration with JAX for use in machine learning pipelines.

This documentation introduces hijax types, a new feature in JAX that allows defining custom types with their own invariants, tangent types, batching, and sharding behavior, illustrated with an example of quantized arrays.

This Ph.D. thesis presents a self-contained textbook on differentiable ray tracing for radio propagation modeling, integrating automatic differentiation (e.g., JAX) into ray tracing pipelines to solve inverse problems and train ML models for next-generation wireless design.

SOLAR is a framework that automatically derives validated speed-of-light performance bounds from PyTorch and JAX source code using an LLM frontend and deterministic analysis, enabling headroom analysis and optimization insights for deep learning workloads.

Trained a superhuman Generals.io agent using self-play reinforcement learning with a JAX-based pipeline and Vision Transformer. Achieved #1 on human 1v1 leaderboard; all code and a fast JAX simulator open-sourced.

This article explains in detail the core ideas of JAX, including function purity, immutability, explicit state management, and JIT compilation, helping readers shift from object-oriented thinking to functional programming to optimize machine learning performance.

A paper on text-to-image generation is released with open-sourced code, models, and full training recipe, comparing performance against other models.

MiniT2I is a minimalist direct-RGB text-to-image generator using a pixel-space MM-JiT denoiser with flow matching and frozen FLAN-T5-Large text tokens, with open-source JAX/Flax and PyTorch implementations released along with checkpoints.

This paper presents acopula, a JAX-native framework for nested Archimedean copula inference that handles arbitrary censoring, nesting trees, and exact parameter gradients using Taylor-mode automatic differentiation, achieving significant speedups over existing methods.

A user demonstrates using OpenAI's Codex to automatically generate a Colab notebook that trains a ~10 million parameter transformer in JAX/Flax/Optax on addition, achieving high accuracy after 4000 steps on a T4 GPU.

This paper introduces Mahjax, a fully vectorized Riichi Mahjong simulator implemented in JAX for GPU-accelerated reinforcement learning, achieving high throughput and enabling tabula rasa training.

NVIDIA and Google Cloud announced new learning paths, codelabs, and livestreams for their joint developer community at Google I/O, aimed at helping AI builders leverage the NVIDIA AI platform on Google Cloud with tools like JAX, NVIDIA Dynamo, and Gemma 4 models.

Progress on jax-js includes new matmul benchmarks, faster real-time TTS demo, improved codegen, and an LLM demo running Gemma 3 270B. jax-js is an open-source web ML framework.