📄 中文摘要
在生产环境中运行vLLM的团队可能会面临多种挑战，包括CUDA内存溢出错误、显卡性能不足以及多模态支持的不稳定等问题。尽管vLLM在某些情况下表现良好，但许多用户仍在寻找更好的替代方案。该指南基于真实的生产经验，涵盖了15种vLLM替代方案，重点关注实际部署中的考虑因素，而非市场营销基准或个人使用体验。这些替代方案旨在帮助团队在大规模运行LLM推理时提高效率和稳定性。
2026年最佳vLLM替代方案：用于生产中的LLM推理
出处: 10 Best vLLM Alternatives for LLM Inference in Production (2026)
发布: 2026年3月12日
📄 English Summary
10 Best vLLM Alternatives for LLM Inference in Production (2026)
Running vLLM in production can present various challenges, including random CUDA out-of-memory errors, insufficient GPU performance, and instability in multi-modal support. While vLLM may work well for some, many users are seeking better alternatives. This guide covers 15 alternatives to vLLM based on real production experiences, focusing on actual deployment considerations rather than marketing benchmarks or personal anecdotes. These alternatives aim to enhance efficiency and stability for teams conducting LLM inference at scale.