jammi-server-cu12

GPU build of the Jammi engine server, packaged as a pip-installable wheel that ships the CUDA-enabled jammi-server binary behind a jammi-server console script.

pip install jammi-server-cu12
jammi-server --help

The CUDA libraries the binary links (libcudart, libcublas, libcublasLt, libcurand, libnvrtc, and libnccl, the collective-communication library the multi-GPU training path uses) are pulled in as nvidia-*-cu12 wheel dependencies; the console script prepends their lib/ directories to LD_LIBRARY_PATH before exec'ing the binary, so no system CUDA install is required (only an NVIDIA driver on the host). This package and jammi-server (CPU) both provide the jammi-server command — install exactly one.

The release tarball

jammi-server-cu12-<version>-x86_64-unknown-linux-gnu.tar.gz (built by .github/workflows/release-binaries.yml's server-cu12-build job) ships the same binary as this wheel, packaged as a self-contained directory with a launcher instead of a console script: it bundles the CUDA runtime shared libraries the binary links, derived from the binary's own transitive DT_NEEDED closure plus a measured floor for dlopen-only dependencies (ci/scripts/bundle_cuda_libs.sh), never a hand-kept name list — a dependency a future feature adds is picked up automatically, and one that cannot be satisfied fails the build by name rather than shipping silently incomplete.

That self-sufficiency claim — the bundled lib/ is EVERYTHING the loader needs on a host with only an NVIDIA driver and no system CUDA install, never relying on a copy the build host happens to also carry — is proven in the release lane by two arms, not asserted by construction: (1) DETECTION runs the real dynamic loader against the real staged binary and refuses any bundle-able member resolved from outside the staged lib/; (2) THE JAIL hides every host copy behind a real chroot, so a bundle-able member has nothing else to resolve from at all. Both arms are required on every build; see bundle_cuda_libs.sh's own module doc for the full argument and the measured defects each arm's shape closes. libnvrtc-builtins (the one dlopen-only member of the floor) is staged but is not, and cannot be, proven by either loader arm — NVRTC loads it at runtime rather than linking it, so no loader trace ever names it.

Requirements

NVIDIA GPU of compute capability 8.0 or newer (Ampere A100, A10/A6000, Ada L4/L40S, Hopper H100). The kernels are built at compute_80 and the driver JIT-forwards them to 8.6 / 8.9 / 9.0. Turing (7.5, e.g. Tesla T4) is not supported by this build.
NVIDIA driver r560 or newer (Linux: ≥ 560.28.03). The kernels ship as PTX compiled with the CUDA 12.6 toolkit, which the deployment driver JIT-compiles at model load; a driver below the CUDA 12.6 line (for example 550.x, which tops out at CUDA 12.4) cannot compile that PTX and the server exits at startup with a clear driver-too-old error rather than a raw CUDA_ERROR_UNSUPPORTED_PTX_VERSION at first inference. nvidia-smi shows the installed driver and its max CUDA version.

Metadata

Release files for jammi-server-cu12 0.53.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for jammi-server-cu12 0.53.0
File	Interpreter	ABI	Platform	Reset
jammi_server_cu12-0.53.0-py3-none-manylinux_2_28_x86_64.whl 72.5 MB Oct 2, 2026	Python 3	none	Linux glibc 2.28+ x86-64	Details

Release files / jammi_server_cu12-0.53.0-py3-none-manylinux_2_28_x86_64.whl

Download URL	jammi_server_cu12-0.53.0-py3-none-manylinux_2_28_x86_64.whl
Size	72.5 MB
Tags	Linux glibc 2.28+ x86-64 Python 3
SHA-256 checksum How to use checksums	`80e2fde6ab7ea35961be3ef807407193fc117be708ca64eb56a028a81101d7d7`
BLAKE2b-256 checksum How to use checksums	`48375aa47df9c4ac81d75a7e5db964ce6874f254290780ff32ebc1a086690a4f`
Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/f-inverse/jammi-ai
Publishing commit: github.com/f-inverse/jammi-ai/tree/b842b1918fce7c07aacbebbeeeb4e36d4bd7c2fd

Workflow

Publishing configuration: github.com/f-inverse/jammi-ai/blob/b842b1918fce7c07aacbebbeeeb4e36d4bd7c2fd/.github/workflows/pypi-server-cuda.yml
Publishing logs: github.com/f-inverse/jammi-ai/actions/runs/37029860026/attempts/1

Artifact

Subject: jammi_server_cu12-0.53.0-py3-none-manylinux_2_28_x86_64.whl
SHA-256 checksum: 80e2fde6ab7ea35961be3ef807407193fc117be708ca64eb56a028a81101d7d7 Verifying attestations