PyAutoLabs · Jammy2211 · Aug 23, 2026 · Aug 23, 2026
diff --git a/skills/ag_build_interferometer_model.md b/skills/ag_build_interferometer_model.md
@@ -275,10 +275,6 @@ not cosmetic.
   10⁹, so a disagreement bigger than that means something about the NUFFT setup is wrong rather
   than the transform being approximate. It is also what the pixelised reconstruction's
   sparse-operator path uses.
-- **`ag.TransformerNUFFTPyNUFFT`** — a legacy `pynufft`-backed transformer, kept as a non-JAX
-  fallback. It is not JAX-traceable, so it forfeits GPU acceleration *and* the gradient-based
-  searches, which need the likelihood's derivatives.
-
 **There is a guard, and it is there for a reason.** `ag.Interferometer` (and `from_fits`) raises
 `raise_error_dft_visibilities_limit=True` by default, and it will refuse to build a dataset with
 more than **10,000 visibilities** while `transformer_class=ag.TransformerDFT`. The DFT at that

diff --git a/skills/ag_setup_environment.md b/skills/ag_setup_environment.md
@@ -104,7 +104,7 @@ question in disguise:
   command names it explicitly. It accelerates the compiled geometry kernels in
   PyAutoArray; if it will not build on your platform, the stack runs without it.
 
-`pynufft` only matters if you fit visibilities — skip it otherwise; the library prints
+`nufftax` only matters if you fit visibilities — skip it otherwise; the library prints
 exactly what to install if you reach interferometer code without it. The full extras
 table, the conda route and the editable-clone route (for reading or modifying library
 source) are in

diff --git a/wiki/core/api/datasets.md b/wiki/core/api/datasets.md
@@ -24,7 +24,7 @@ sources:
       - scripts/interferometer/start_here.py
     pinned_commit: d6db2643b9f2cd418efc9473f560dc2a2d459c73
 last_updated: 2026-08-01
-content_sha256: 588526e9facb90fe46fc488fbece00c62fc2a5640fa9cf01ff0ebf92243fafd8
+content_sha256: 9af41974373a9870baadb95596c1a6ebba5daf770c26c16f7293459a9afd91cb
 ---
 
 # Datasets
@@ -181,8 +181,8 @@ Adapted from `autogalaxy_workspace:scripts/interferometer/start_here.py`. Source
 
 **Transformers.** `ag.TransformerNUFFT` is the JAX-native non-uniform FFT and the recommended
 default at any visibility count. `ag.TransformerDFT` is an exact discrete transform — slower
-for large `n_vis`, but useful as a verification reference. `ag.TransformerNUFFTPyNUFFT` is the
-legacy backend, available when explicitly requested.
+for large `n_vis`, but useful as a verification reference, and the transformer to use where
+JAX is unavailable (notably Intel macOS, for which JAX ships no wheels).
 
 Attributes:
 

diff --git a/wiki/core/concepts/interferometer_theory.md b/wiki/core/concepts/interferometer_theory.md
@@ -20,7 +20,7 @@ sources:
       - scripts/interferometer/features/linear_light_profiles/modeling.py
     pinned_commit: d6db2643b9f2cd418efc9473f560dc2a2d459c73
 last_updated: 2026-08-01
-content_sha256: 8b874623d54019876858f6ca351df0359ae461d3fc6f43d3b132fd2e2284825c
+content_sha256: 1fa1de69f41b378b2a1a99b1d0d77bc0f935e30f0ecf19b67a5bcfa437759593
 ---
 
 # Interferometer fitting — visibilities, the uv-plane and dirty images
@@ -120,10 +120,6 @@ Three transformers are available (`PyAutoArray:autoarray/operators/transformer.p
 - **`ag.TransformerDFT`** — the exact discrete Fourier transform. Slower than the NUFFT once
   `n_vis` is large, but valuable as a reference for verifying a NUFFT result, and used by the
   pixelised reconstruction's sparse-operator path.
-- **`ag.TransformerNUFFTPyNUFFT`** — a legacy `pynufft`-backed transformer, kept as a
-  non-JAX fallback. It is not JAX-traceable, so it forfeits GPU acceleration and the
-  gradient-based searches.
-
 Because `nufftax` is JAX-native, light-profile interferometer fitting now runs at full GPU
 speed for datasets with **arbitrarily many visibilities** — up to the tens or hundreds of
 millions typical of high-resolution ALMA observations.

diff --git a/wiki/core/operations/installation.md b/wiki/core/operations/installation.md
@@ -36,7 +36,7 @@ sources:
       - autoassistant/audit_skill_apis.py
     pinned_commit: ed72fabb33e14a9a701a4d280e8775dd3a20e98c
 last_updated: 2026-08-01
-content_sha256: a758c9de0a2e8e2a9e880fed2f3562a8918feec447616c6eb2c94c667a6c81ae
+content_sha256: 8999fa2a4700ae1fe2db2782fe66bdb222eadf78d35db474b6043da47155167c
 ---
 
 # Installation
@@ -100,7 +100,7 @@ below need a modern resolver.
 | Extra | Pulls in | Install it when |
 |---|---|---|
 | `jax` | `autofit[jax]` (→ `autonerves[jax]`: `jax`/`jaxlib` `>=0.7.0,<0.11.0`, `jaxnnls`; plus `optax`) and `jax_zero_contour` | Almost always — JAX is the accelerated evaluation path, on CPU and GPU |
-| `optional` | `autogalaxy[jax]`, `numba`, `pynufft`, `zeus-mcmc`, `getdist` | You want the full set in one command |
+| `optional` | `autogalaxy[jax]`, `numba`, `zeus-mcmc`, `getdist` | You want the full set in one command |
 | `test` | `pytest`, `colossus` | You are running PyAutoGalaxy's own test suite |
 | `docs` | Sphinx + theme packages | You are building the RTD site |
 
@@ -115,9 +115,10 @@ the CPU wheel over it.
 JIT-compiled geometry kernels in PyAutoArray. If it will not build on your platform,
 PyAutoGalaxy runs without it — see `PyAutoGalaxy:docs/installation/numba.md`.
 
-**`pynufft` is only for interferometer work.** Skip it unless you fit visibilities; if
-you do run interferometer code without it, the library prints a message telling you
-exactly what to install.
+**`nufftax` is only for interferometer work.** It is not in PyAutoGalaxy's `optional`
+extra — install it explicitly with `pip install nufftax`. Skip it unless you fit
+visibilities; if you do run interferometer code without it, the library prints a message
+telling you exactly what to install.
 
 ### Conda
 

diff --git a/wiki/core/stack/autoarray.md b/wiki/core/stack/autoarray.md
@@ -13,7 +13,7 @@ sources:
       - README.md
     pinned_commit: 59b0f198fc7bdf9c91e5a8f734dad796fcc55656
 last_updated: 2026-08-01
-content_sha256: 8f4425d067e527e01d00fa5a512a91dcf96de18071acb8cb5adfe1b43fe4e9a0
+content_sha256: f4a8f8720dde3b84912f3220ae2deeeeeb2cb5c912d5cc5177e88f9663244793
 ---
 
 # PyAutoArray — arrays, grids, masks, datasets
@@ -90,7 +90,7 @@ The plot-label notation and output settings a user is more likely to edit live i
 
 `autonerves`, `astropy`, `decorator`, `dill`, `matplotlib`, `scipy`, `scikit-image`,
 `scikit-learn`, `tqdm`. Optional extras add `numba` for JIT-acceleration of geometry
-kernels, and `nufftax` / `pynufft` for visibility transforms.
+kernels, and `nufftax` for visibility transforms.
 
 ## See also
 

diff --git a/wiki/core/stack/autogalaxy.md b/wiki/core/stack/autogalaxy.md
@@ -17,7 +17,7 @@ sources:
       - README.md
     pinned_commit: 65b14d7767da194a21bf0f3a4345f0790af86ed4
 last_updated: 2026-08-01
-content_sha256: edcdfaee1873806090b90e1a7537646c94fd2c78e699ffaf3ab447ef3f3bb84d
+content_sha256: c9cb7e2d004800e67686a35927da1463da9cab032379360200c0269381566983
 ---
 
 # PyAutoGalaxy — galaxy structure and galaxy modelling
@@ -177,7 +177,7 @@ write `af.Model(ag.lp.Sersic)`, the default prior for each parameter comes from
 ## Dependencies
 
 `autofit`, `autoarray`, `astropy`, `nautilus-sampler`. Optional extras add `numba`,
-`pynufft`, `zeus-mcmc`, `getdist`, and — via `autogalaxy[jax]` — `autofit[jax]`
+`zeus-mcmc`, `getdist`, and — via `autogalaxy[jax]` — `autofit[jax]`
 (JAX, jaxlib, jaxnnls, optax) plus `jax_zero_contour`.
 
 ## See also