The vllm-project organization on GitHub features a wide range of public repositories focused on large language model (LLM) inference and deployment. Notable projects include vllm, a high-throughput inference engine, and vllm-omni, a framework for efficient model inference. The organization primarily utilizes programming languages such as Python, C++, Rust, Go, HTML, and TypeScript.
Top languages
Public repositories
vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
vllm-omni
A framework for efficient model inference with omni-modality models
aibrix
Cost-efficient and pluggable Infrastructure components for GenAI inference
semantic-router
System Level Intelligent Router for Mixture-of-Models at Cloud, Data Center and Edge
llm-compressor
Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM
production-stack
vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization
vllm-ascend
Community maintained hardware plugin for vLLM on Ascend
vllm-metal
Community maintained hardware plugin for vLLM on Apple Silicon
guidellm
Evaluate and Enhance Your LLM Deployments for Real-World Inference Needs
recipes
Common recipes to run vLLM
speculators
A unified library for building, evaluating, and storing speculative decoding algorithms for LLM inference in vLLM
tpu-inference
TPU inference for vLLM, with unified JAX and PyTorch support.
compressed-tensors
A safetensors extension to efficiently store sparse quantized tensors on disk
router
A high-performance and light-weight router for vLLM large scale deployment
vime
An LLM post-training framework with vLLM for RL Scaling
flash-attention
Fast and memory-efficient exact attention
vllm-skills
Agent skills for vLLM
vllm-openvino
No description provided for this repository.
vllm-daily
vLLM Daily Summarization of Merged PRs
vllm-xpu-kernels
The vLLM XPU kernels for Intel GPU
vllm-project.github.io
No description provided for this repository.
ci-infra
This repo hosts code for vLLM CI & Performance Benchmark infrastructure.
vllm-gaudi
Community maintained hardware plugin for vLLM on Intel Gaudi
agentic-api
Stateful API logic for agentic applications using vLLM
vllm-neuron
Community maintained hardware plugin for vLLM on AWS Neuron
dllm-plugin
vLLM plugin for block-based diffusion language model (dLLM) support
vllm-nccl
Manages vllm-nccl dependency
FlashMLA
No description provided for this repository.
bart-plugin
vLLM Model plugin for the encoder-decoder BART model
vLLM-in-PyTorch-Conference-2025
No description provided for this repository.
media-kit
vLLM Logo Assets
vllm-project.github.io-static
No description provided for this repository.
vllm-gguf-plugin
vLLM Quantization plugin for GGUF
perf-eval
Performance benchmark & accuracy evaluation for vLLM
vllm-dashboard
No description provided for this repository.
perf-dashboard
Performance dashboard for vLLM
vllm-bnb-plugin
vLLM Quantization plugin for bitsandbytes
rfcs
No description provided for this repository.
MSA
No description provided for this repository.
DeepGEMM
DeepGEMM: clean and efficient FP8 GEMM kernels with fine-grained scaling
vllm-docs
No description provided for this repository.
llm-multimodal
Standalone fork of llm-multimodal from SMG
Frequently asked questions
What does vllm-project build on GitHub?
vllm-project develops tools and frameworks for large language model inference and deployment. Key repositories include vllm, a high-throughput serving engine, and aibrix, which provides cost-efficient infrastructure components.
Which programming languages does vllm-project use?
The primary programming languages used by vllm-project are Python, C++, Rust, Go, HTML, and TypeScript. These languages support the development of various tools and frameworks for LLMs.
Are vllm-project's repositories public?
Yes, all repositories of vllm-project are public on GitHub. This allows users and developers to access, contribute to, and collaborate on projects related to LLM inference and deployment.
Is this exposure intended?
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