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waqasm86/
kaggle-vllm-binaries

Name: kaggle-vllm-binaries
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vLLM Kaggle validated binaries

This repository distributes a vLLM wheel built and functionally validated on a Kaggle Notebook with two NVIDIA Tesla T4 (SM75) GPUs. It is packaging work around upstream vLLM, not a fork or a claim of ownership over vLLM. This is not an official upstream vLLM binary.

The repository also carries exact PyPI artifacts for the lightweight kaggle-vllm 0.1.2 SDK, plus the historical 0.1.0 and 0.1.1 SDK files. These pure-Python artifacts do not contain vLLM, CUDA, or Torch and do not install the native runtime implicitly.

kaggle-vllm 0.2.0

The public kaggle-vllm 0.2.0 SDK continues to resolve the same immutable native wheel documented below. No native object was rebuilt, replaced, or re-uploaded for the SDK release. Development-candidate acceptance on 2026-08-30 passed on the exact dual-T4 profile; final acceptance against the public package remains pending.

The exact PyPI artifacts are:

Public PyPI file	Size	SHA256
`kaggle_vllm-0.2.0-py3-none-any.whl`	36,732 bytes	`f3dce393c9e0bd43b9ba29a29ae14f9467857e5eea61390d41f512a52911fbbe`
`kaggle_vllm-0.2.0.tar.gz`	43,104 bytes	`48ed97da07e54119939053e38f4e87900cf79316711d7b4558aed8122a66e3aa`

These 0.2.0 SDK files are authoritative on PyPI and are not duplicated in this repository. Their publication does not change the native wheel identity or immutable revision.

Historical mirrored SDK artifacts

File	SHA256
`kaggle_vllm-0.1.2-py3-none-any.whl`	`13f1043df4a173e74555c6a4d7a8f66b4e661d942fc0222124208f50b1e9aad2`
`kaggle_vllm-0.1.2.tar.gz`	`f05c210985bcc74fad689a1ffaf0c4200e80041e791d902e1703b279db02679e`
`kaggle_vllm-0.1.1-py3-none-any.whl`	`d8dfb58e369ceea90b2ade10c75d7678166615a04cbea120855bfd2329bbc9db`
`kaggle_vllm-0.1.1.tar.gz`	`c9981a564513b596bdbd0a68365230d2eb330a61b6b28e42fc22c043b5169349`
`kaggle_vllm-0.1.0-py3-none-any.whl`	`e6b525d03257f24e2e062770763bf060042fe4868f879fb6f81efc722b076233`
`kaggle_vllm-0.1.0.tar.gz`	`a35776573291846f20747dad17e193bd00b4dbb4853294224f17db045c51dd0a`

The primary installation source is PyPI:

bash

pip install "kaggle-vllm[hub]==0.2.0"
kaggle-vllm bootstrap

The latest immutable Hub SDK fallback is the historical byte-identical 0.1.2 PyPI wheel:

bash

pip install "https://huggingface.co/waqasm86/kaggle-vllm-binaries/resolve/97b741d7fc988ed557a00fc28f2e34abad09fb7d/kaggle_vllm-0.1.2-py3-none-any.whl#sha256=13f1043df4a173e74555c6a4d7a8f66b4e661d942fc0222124208f50b1e9aad2"
kaggle-vllm bootstrap

The historical 0.1.1 fallback remains available at revision ff213d775c560645dbd1bdaf86f7412005717969 with its recorded wheel checksum.

The historical 0.1.0 fallback remains pinned to its original publication commit:

bash

pip install "https://huggingface.co/waqasm86/kaggle-vllm-binaries/resolve/ec75826d10e2dbc3c94c4682342ea3b65d7b72e2/kaggle_vllm-0.1.0-py3-none-any.whl#sha256=e6b525d03257f24e2e062770763bf060042fe4868f879fb6f81efc722b076233"
kaggle-vllm bootstrap

The bootstrap profile remains pinned to the native wheel revision and checksum; it does not use mutable main for native delivery.

Binary identity

Source: upstream vLLM v0.18.1
Commit: a26e8dc7ff2111a005144d775ecf9cebf56c45b2
Wheel version: 0.18.2.dev0+ga26e8dc7f.d20260822.cu128
ABI: CPython 3.12, Linux x86_64
SHA256: 5a9bd710b8a19fdd23abb3442baad892da977466f996334decd533a225f5fd0c

The differing source and wheel version strings result from setuptools_scm metadata generation; the source checkout was the v0.18.1 tag.

The native runtime's generated 0.18.2.dev0+... distribution version is independent of the lightweight kaggle-vllm SDK version. Reusing this wheel for SDK 0.2.0 does not make the native source upstream vLLM v0.18.2.

Validated runtime

Python 3.12.13, PyTorch 2.10.0+cu128, CUDA toolkit 12.8.93, driver 580.159.04, NCCL 2.27.5, and two Tesla T4 GPUs (SM75). Validation covered native imports, single-GPU inference, NCCL, TP=2 inference, Qwen2.5-3B FP16, vLLM sharded_state persistence/reload, and OpenAI-compatible serving.

FlashAttention 2 is unavailable on the Tesla T4's SM75 architecture. During the validated runs, vLLM selected TRITON_ATTN successfully. SymmMem capability warnings are expected on SM75; ordinary NCCL tensor-parallel communication still worked.

Use the supplied checksums and compatibility JSON before staging. Avoid normal dependency resolution that could replace Kaggle's Torch stack; the associated kaggle-vllm project documents explicit, checksum-verified pip --target --no-deps bootstrap staging.

The wheel includes upstream vLLM's Apache-2.0 license. Compatibility beyond the documented environment is not claimed. In particular, this artifact is not a claim of universal CUDA, Python, PyTorch, GPU, or platform compatibility.