Ray vs vLLM
Side-by-side comparison of two AI agent tools
Short answer
- vLLM is growing faster: +2,942 GitHub stars in the last 30 days vs +330 for Ray.
- Pick Ray for: ray is an AI compute engine. Pick vLLM for: a high-throughput and memory-efficient inference and serving engine for LLMs.
From GitHub data refreshed daily.
Rayopen-source
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
vLLMopen-source
A high-throughput and memory-efficient inference and serving engine for LLMs
Metrics
| Ray | vLLM | |
|---|---|---|
| Stars | 44.0k | 93.1k |
| Star velocity /mo | 330.31746031746036 | 2.9k |
| Commits (90d) | 1.0k | 4.0k |
| Releases (6m) | 7 | 10 |
| Overall score | 0.773229438636488 | 0.9292412178941084 |
Pros
- +统一的分布式框架，将数据处理、训练、调优和服务集成在单一平台中，减少了技术栈复杂性和学习成本
- +平台无关设计，支持从本地开发到云端生产的无缝部署，兼容所有主流云提供商和Kubernetes环境
- +强大的生态系统，拥有41000+GitHub星数和活跃的社区，提供丰富的集成和扩展能力
- +Exceptional serving throughput with PagedAttention memory optimization and continuous batching for production-scale LLM deployment
- +Comprehensive hardware support across NVIDIA, AMD, Intel platforms and specialized accelerators with flexible parallelism options
- +Seamless Hugging Face integration with OpenAI-compatible API server for easy model deployment and switching
Cons
- -分布式系统的学习曲线较陡峭，需要理解分布式计算概念和Ray特有的编程模式
- -对于简单的单机任务可能存在过度工程化的问题，引入了不必要的复杂性
- -资源消耗较高，运行分布式集群需要相当的内存和计算资源投入
- -Requires significant GPU memory for optimal performance, limiting accessibility for resource-constrained environments
- -Complex setup and configuration for distributed inference across multiple GPUs or nodes
- -Primary focus on inference means limited support for training or fine-tuning workflows
Use Cases
- •大规模机器学习训练：利用Train库在多GPU/多节点环境下进行深度学习模型的分布式训练，显著缩短训练时间
- •超参数优化：使用Tune库对机器学习模型进行大规模并行的超参数搜索和调优，找到最优模型配置
- •强化学习应用：通过RLlib构建和训练复杂的强化学习算法，适用于游戏AI、机器人控制和自动化决策系统
- •Production API serving for applications requiring high-throughput LLM inference with multiple concurrent users
- •Research and experimentation with open-source LLMs requiring efficient model switching and testing
- •Enterprise deployment of private LLM services with OpenAI-compatible interfaces for existing applications
FAQ
- Which is more popular, Ray or vLLM?
- vLLM has more GitHub stars (93,060 vs 43,963).
- Which is more actively developed, Ray or vLLM?
- vLLM had more commits in the last 90 days (3,992 vs 1,028).
- Should I use Ray or vLLM?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.