diff --git a/docs/RELEASE_CONTENTS.md b/docs/RELEASE_CONTENTS.md index f6d66754b..c05c8d271 100644 --- a/docs/RELEASE_CONTENTS.md +++ b/docs/RELEASE_CONTENTS.md @@ -113,7 +113,8 @@ is no CUDA-toolkit version requirement on the host. | any `cpu` / `nocuda` variant, and everything for macOS | — | — | none | The aarch64 build is cross-compiled and verified in CI to be Arm, self-contained and to carry both -GPU targets, but it is **not exercised on hardware** — CI has no GH200 or Spark runner. +GPU targets. CI has no Arm runner; rugnux itself is tested on a DGX Spark (GB10), where the timing +benchmark passes with a native build of the same source. **A V100 needs the CUDA 12 build.** CUDA 13 dropped offline compilation for Volta, and the PTX that a fatbin also carries only ever JIT-compiles *forwards*, so a CUDA 13 artefact contains nothing a V100 diff --git a/docs/RUGNUX_INSTALL.md b/docs/RUGNUX_INSTALL.md index e4169e97b..5358f4a8a 100644 --- a/docs/RUGNUX_INSTALL.md +++ b/docs/RUGNUX_INSTALL.md @@ -36,9 +36,9 @@ take the archive for your architecture from the Gitea release page: | Archive | For | | --- | --- | | `rugnux--linux-x86_64-cuda12.tgz` | 64-bit Intel/AMD Linux. Built on RHEL 8, so it runs on any newer Linux | -| `rugnux--linux-aarch64-cuda13.tgz` | 64-bit Arm Linux — NVIDIA GH200 and DGX Spark. Built on Ubuntu 24.04, so it needs glibc 2.39 or newer. Cross-compiled and **not yet exercised on Arm hardware** | +| `rugnux--linux-aarch64-cuda13.tgz` | 64-bit Arm Linux — NVIDIA GH200 and DGX Spark. Built on Ubuntu 24.04, so it needs glibc 2.39 or newer. Rugnux is tested on a DGX Spark (GB10): the timing benchmark passes with a native build of the same source; the archive itself is cross-compiled in CI | | `rugnux--win64-cuda13.zip` | 64-bit Windows | -| `rugnux--macos-arm64-cpu.tgz` | macOS 13 (Ventura) or newer on Apple Silicon (M1 and newer). No CUDA on macOS: CPU, with Bragg integration and parts of molecular replacement on the Apple GPU (Metal). Intel Macs are not supported | +| `rugnux--macos-arm64-cpu.tgz` | macOS 13 (Ventura) or newer on Apple Silicon (M1 and newer). No CUDA on macOS: CPU, with Bragg integration, the model density and the molecular-replacement rotation function on the Apple GPU (Metal). Tested on an Apple M-series Mac mini, where the timing benchmark passes on Metal and CPU-only. Intel Macs are not supported | **The archive has no top-level directory** — it unpacks straight into `bin/` and `share/`. Always give `tar` a destination of its own, or it will scatter those into whatever directory you are in: