pass bool | exit_code int64 | elapsed_seconds float64 | no_secret_injected bool | repo_id string | payload_revision string | archive_sha256 string | all_manifest_files_verified bool | gpu_used bool |
|---|---|---|---|---|---|---|---|---|
true | 0 | 58.373189 | true | codemaivanngu/vllm-portable-runtime | 2f96debc23c2ae0ca333314587bf321b28c25f37 | b22e2ef9b1d1f1f2f233b9360bc82e8cb53b69c3d4811e3043167f0037db532b | true | false |
Portable vLLM 0.32.0 / CUDA 13.0
Release vllm-0.32.0-cu130-20261028-r1. Payload revision: 2f96debc23c2ae0ca333314587bf321b28c25f37.
Standalone Python 3.12.12, vLLM 0.32.0, Torch 2.13.0, an exact uv.lock,
private CUDA 13.0 toolkit, support libraries and node helpers.
Linux x86_64, glibc >= 2.35. Fixed installation root:
/opt/vllm-runtime-0.32.0-cu130 (root permission required).
Archive size: 5,821,293,851 bytes.
Unpacked regular-file bytes: 11,041,486,896.
Archive SHA256: b22e2ef9b1d1f1f2f233b9360bc82e8cb53b69c3d4811e3043167f0037db532b.
Reserve space for the downloaded archive, extraction, JIT caches, models and outputs.
Download
Download download_release.py. With an existing Python 3.10+ on the company Linux instance:
python3 download_release.py --revision 2f96debc23c2ae0ca333314587bf321b28c25f37 --archive-sha256 b22e2ef9b1d1f1f2f233b9360bc82e8cb53b69c3d4811e3043167f0037db532b --output /your/node-local/vllm-payload --workers 8
Use your existing approved proxy environment. This public release requires no
HF token. The downloader verifies every manifest file; it neither installs nor
uses GPUs. From the payload directory, verify sha256sum -c SHA256SUMS, then
run python3 install.py. The installer refuses existing paths and runs CPU/import
checks with GPUs hidden. It changes no driver or system CUDA installation.
Run
The foreground CLI wrapper is
/opt/vllm-runtime-0.32.0-cu130/node/vllm.sh; pass normal vLLM arguments such as
serve /path/to/your/model --host 127.0.0.1 --port 8000.
node/python-vllm.sh runs Python scripts in the same independent runtime.
Both wrappers default to GPUs hidden. Explicitly set CUDA_VISIBLE_DEVICES
to your assigned GPU UUID(s), following your scheduler's assignment.
Stopping the server and its owned workers releases their VRAM; shell
deactivation alone does not stop processes.
Production models/data/output and JIT caches remain external. HF defaults to
offline mode; stage weights separately. Override VLLM_PORTABLE_CACHE for the
cache directory and VLLM_PORTABLE_HF_HOME for the model cache.
The node still needs a compatible NVIDIA driver, host glibc and a C/C++
compiler with development headers (GCC 11 was tested). nvcc/CUDA headers/ptxas
come from the private bundled toolkit. No NVIDIA kernel driver or host glibc
is bundled. Third-party licenses and CUDA notices remain applicable; this
bundle does not relicense dependencies.
Evidence and limits
Publication requires CPU checks, native B200 CUDA kernels, normal compiled
synthetic dense BF16 generation and loopback HTTP serving, plus archive restore
and repeated GPU inference/HTTP checks on a separate Ubuntu 22.04 image with
no system CUDA toolkit and Internet blocked. See qualification.json, both restore receipts,
build-facts.json, manifest.json and uv.lock under releases/vllm-0.32.0-cu130-20261028-r1/.
Fixtures are tiny random models, not production weights or benchmark results.
The company node, real model families, multi-GPU/NCCL, quantization, multimodal
and all optional backends have not been qualified by these tests.
The release contains no corporate data, credentials or run caches.
A full anonymous download of the archive and every manifest file passed SHA256 verification. The receipt is published under validation/vllm-0.32.0-cu130-20261028-r1/public-download-receipt.json.
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