ppc64le/linux/: vllm-0.18.1+cpuppc64le1 metadata and description

A high-throughput and memory-efficient inference and serving engine for LLMs

author	vLLM Team
classifiers	Programming Language :: Python :: 3.10 Programming Language :: Python :: 3.11 Programming Language :: Python :: 3.12 Programming Language :: Python :: 3.13 Intended Audience :: Developers Intended Audience :: Information Technology Intended Audience :: Science/Research Topic :: Scientific/Engineering :: Artificial Intelligence Topic :: Scientific/Engineering :: Information Analysis Environment :: MetaData :: IBM Python Ecosystem
description_content_type	text/markdown
dynamic	license-file provides-extra requires-dist
license_expression	Apache-2.0
license_file	LICENSE
metadata_version	2.4
project_urls	Homepage, https://github.com/vllm-project/vllm Documentation, https://docs.vllm.ai/en/latest/ Slack, https://slack.vllm.ai/
provides_extras	zen bench tensorizer fastsafetensors instanttensor runai audio video flashinfer petit-kernel helion grpc otel
requires_dist	regex cachetools psutil sentencepiece numpy requests>=2.26.0 tqdm blake3 py-cpuinfo transformers<5,>=4.56.0 tokenizers>=0.21.1 protobuf!=6.30.,!=6.31.,!=6.32.,!=6.33.0.,!=6.33.1.,!=6.33.2.,!=6.33.3.,!=6.33.4.,>=5.29.6 fastapi[standard]>=0.115.0 aiohttp>=3.13.3 openai<2.25.0,>=1.99.1 pydantic>=2.12.0 prometheus_client>=0.18.0 pillow prometheus-fastapi-instrumentator>=7.0.0 tiktoken>=0.6.0 lm-format-enforcer==0.11.3 llguidance<1.4.0,>=1.3.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64" or platform_machine == "s390x" or platform_machine == "ppc64le" outlines_core==0.2.11 diskcache==5.6.3 lark==1.2.2 xgrammar<1.0.0,>=0.1.32; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64" or platform_machine == "s390x" or platform_machine == "ppc64le" typing_extensions>=4.10 filelock>=3.16.1 partial-json-parser pyzmq>=25.0.0 msgspec gguf>=0.17.0 mistral_common[image]>=1.10.0 opencv-python-headless>=4.13.0 pyyaml six>=1.16.0; python_version > "3.11" setuptools<81.0.0,>=77.0.3; python_version > "3.11" einops compressed-tensors==0.13.0 depyf==0.20.0 cloudpickle watchfiles python-json-logger ninja pybase64 cbor2 ijson setproctitle openai-harmony>=0.0.3 anthropic>=0.71.0 model-hosting-container-standards<1.0.0,>=0.1.13 mcp opentelemetry-sdk>=1.27.0 opentelemetry-api>=1.27.0 opentelemetry-exporter-otlp>=1.27.0 opentelemetry-semantic-conventions-ai>=0.4.1 setuptools==77.0.3 numba==0.61.2; platform_machine != "s390x" torch==2.10.0+cpu; platform_machine == "x86_64" or platform_machine == "s390x" torch==2.10.0; platform_machine == "aarch64" or platform_system == "Darwin" or platform_machine == "ppc64le" or platform_machine == "riscv64" torchaudio; platform_machine != "s390x" and platform_machine != "riscv64" intel-openmp==2024.2.1; platform_machine == "x86_64" py-cpuinfo; platform_machine == "aarch64" zentorch; extra == "zen" pandas; extra == "bench" matplotlib; extra == "bench" seaborn; extra == "bench" datasets; extra == "bench" scipy; extra == "bench" plotly; extra == "bench" tensorizer==2.10.1; extra == "tensorizer" fastsafetensors>=0.2.2; extra == "fastsafetensors" instanttensor>=0.1.5; extra == "instanttensor" runai-model-streamer[azure,gcs,s3]>=0.15.7; extra == "runai" librosa; extra == "audio" scipy; extra == "audio" soundfile; extra == "audio" mistral_common[audio]; extra == "audio" av; extra == "audio" petit-kernel; extra == "petit-kernel" helion==0.3.2; extra == "helion" smg-grpc-servicer[vllm]>=0.5.0; extra == "grpc" opentelemetry-sdk>=1.26.0; extra == "otel" opentelemetry-api>=1.26.0; extra == "otel" opentelemetry-exporter-otlp>=1.26.0; extra == "otel" opentelemetry-semantic-conventions-ai>=0.4.1; extra == "otel"
requires_python	<3.14,>=3.10

File	Tox results	History
vllm-0.18.1+cpuppc64le1-cp311-cp311-manylinux_2_34_ppc64le.whl Size 35 MB Type Python Wheel Python 3.11		Uploaded to ppc64le/linux by ppc64le 2026-05-05 15:46:33
vllm-0.18.1+cpuppc64le1-cp312-cp312-manylinux_2_34_ppc64le.whl Size 35 MB Type Python Wheel Python 3.12		Uploaded to ppc64le/linux by ppc64le 2026-05-05 15:46:35

Easy, fast, and cheap LLM serving for everyone

🔥 We have built a vllm website to help you get started with vllm. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us.

About

vLLM is a fast and easy-to-use library for LLM inference and serving.

Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

vLLM is fast with:

State-of-the-art serving throughput
Efficient management of attention key and value memory with PagedAttention
Continuous batching of incoming requests
Fast model execution with CUDA/HIP graph
Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
Speculative decoding
Chunked prefill

vLLM is flexible and easy to use with:

Seamless integration with popular Hugging Face models
High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
Tensor, pipeline, data and expert parallelism support for distributed inference
Streaming outputs
OpenAI-compatible API server
Support for NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, Arm CPUs, and TPU. Additionally, support for diverse hardware plugins such as Intel Gaudi, IBM Spyre and Huawei Ascend.
Prefix caching support
Multi-LoRA support

vLLM seamlessly supports most popular open-source models on HuggingFace, including:

Transformer-like LLMs (e.g., Llama)
Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
Embedding Models (e.g., E5-Mistral)
Multi-modal LLMs (e.g., LLaVA)

Find the full list of supported models here.

Getting Started

Install vLLM with pip or from source:

pip install vllm

Visit our documentation to learn more.

Contributing

We welcome and value any contributions and collaborations. Please check out Contributing to vLLM for how to get involved.

Citation

If you use vLLM for your research, please cite our paper:

@inproceedings{kwon2023efficient,
  title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
  author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
  booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
  year={2023}
}

Contact Us

For technical questions and feature requests, please use GitHub Issues
For discussing with fellow users, please use the vLLM Forum
For coordinating contributions and development, please use Slack
For security disclosures, please use GitHub's Security Advisories feature
For collaborations and partnerships, please contact us at [email protected]

Media Kit

If you wish to use vLLM's logo, please refer to our media kit repo

devpi