Docs: say which build pip installs from PyPI

README.md

@@ -83,6 +83,11 @@ Install the latest stable Python package in a Python 3.11–3.15 environment:
 pip install executorch
 ```
 
+On Linux this is the CUDA build, matching the `torch` package on PyPI. On macOS
+and Windows it is the CPU build. For the CPU-only Linux build or another CUDA
+version, see
+[Getting Started](https://docs.pytorch.org/executorch/main/getting-started.html).
+
 Install a nightly built from `main` to use the newest features:
 
 ```bash

docs/source/getting-started.md

@@ -44,6 +44,12 @@ across minor versions but not across major ones. There is no package for CUDA 12
 so on CUDA 12 use the `cpu` variant or build from source. On CUDA 13.0 or 13.1 no
 lower variant exists, so use `cu132`.
 
+Without `--index-url`, pip takes both packages from PyPI, which follows the same
+layout for both. On Linux they are the CUDA build, for the CUDA version PyTorch
+publishes there. On macOS and Windows they are the CPU build. The Linux packages
+also work on a machine without a GPU, but they download the CUDA libraries. Use
+the `cpu` variant to skip them.
+
 To get a change that has landed on the `main` branch but is not in a release
 yet, use a nightly build. These are rebuilt every day. Put `nightly/` in front
 of the variant name, for example `nightly/cu132`, and add `--pre` to the

docs/source/using-executorch-cpp.md

@@ -423,7 +423,8 @@ event tracer.
 
 ### Running on a GPU with the CUDA package
 
-The CUDA build is a separate package. Releases cover CUDA 13.2 and 13.4, so pick the index
+On Linux, the package on PyPI is the CUDA build, for the same CUDA version as the `torch` package
+there. Releases also cover CUDA 13.2 and 13.4 on their own indexes, so you can pick the one
 matching the CUDA version you have (`cu132` or `cu134`). For CUDA 13.2:
 
 ```