Ollama vs vLLM
Side-by-side comparison of two AI agent tools
Short answer
- Pick Ollama for: get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. Pick vLLM for: a high-throughput and memory-efficient inference and serving engine for LLMs.
From GitHub data refreshed daily.
Ollamaopen-source
Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
vLLMopen-source
A high-throughput and memory-efficient inference and serving engine for LLMs
Metrics
| Ollama | vLLM | |
|---|---|---|
| Stars | 182.1k | 93.1k |
| Star velocity /mo | 2.5k | 2.9k |
| Commits (90d) | 297 | 4.0k |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8565267401746319 | 0.9292412178941084 |
Pros
- +完全本地运行，确保数据隐私和安全，无需将敏感信息发送到外部服务器
- +支持广泛的开源模型生态，包括最新的 Kimi-K2.5、GLM-5、DeepSeek 等前沿模型
- +丰富的集成生态系统，可与 Claude Code、OpenClaw 等工具连接，快速构建跨平台 AI 应用
- +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
- -依赖本地计算资源，运行大型模型需要较高的 CPU/GPU 和内存配置
- -模型推理速度受限于本地硬件性能，可能不如云端专用硬件快
- -需要手动管理模型版本更新和依赖关系
- -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
- •企业级私有部署，在内网环境中运行大语言模型，确保敏感数据不外泄
- •开发者工具集成，通过 Claude Code 等编码助手在本地环境中获得 AI 代码建议
- •多平台聊天机器人开发，使用 OpenClaw 将本地模型部署到 Slack、Discord 等通讯平台
- •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, Ollama or vLLM?
- Ollama has more GitHub stars (182,051 vs 93,060).
- Which is more actively developed, Ollama or vLLM?
- vLLM had more commits in the last 90 days (3,992 vs 297).
- Should I use Ollama 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.