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UPDATE: Latest JAX version is now 2025.11.5.1 This release marks the completion of two years work implementing JAX (https://docs.jax.dev/en/latest/notebooks/thinking_in_jax.html) in PyAutoGalaxy. With JAX, any modeling analysis can be run on GPU, with speed up of ~x50 or more. Core Release The core PyAutoGalaxy API does not change significantly, however existing users redownload the new autogalaxy workspace, which has new configs and examples: https://github.com/Jammy2211/autogalaxy_workspace New user should checkout the start_here.ipynb notebook, which can be read via a Google Colab by clicking the hyperlink. GPU Modeling Examples The following Juypter Notebooks, which run via Google Colab, illustrate < 10 minute galaxy modeling for different science cases: start_here_imaging.ipynb: Galaxy-scale strong galaxyes observed with CCD imaging (e.g. Hubble, James Webb). start_here_interferometer.ipynb: Galaxy scale strong galaxyes observed with interferometer data (e.g. ALMA). start_here_multi_wavelength.ipynb: Model multiple images (different wavelength imaging, imaging + interferometer) simultaneously. Performance Of Features Interferometer with many Visibilities: Above ~ GPU uv-plane analysis with hundreds of millions of visibilities and extremely high resolutions run in under and hour, a monumental speed up compared to CPU modeling. Pixelized sources run ~x5 - x20 faster on modern HPC GPU clusters, with galaxy modeling times typically ~10 - 20 minutes. Pixelized source performance depends on the available GPU VRAM.