Many of our innovative ideas have been inspired by academic research, including works such as [Preble](https://arxiv.org/abs/2407.00023), [Melange](https://arxiv.org/abs/2404.14527), [QLM](https://dl.acm.org/doi/10.1145/3698038.3698523) and [MoonCake](https://arxiv.org/abs/2407.00079). Integrating cutting-edge research into a production-grade system has been an enriching journey, enabling us to transform theoretical concepts into real-world applications. These contributions have significantly influenced our work, and we sincerely appreciate the researchers behind them—thank you! We'd also like to thank the vLLM community for their support in making AIBrix the control plane for vLLM, further strengthening our mission to build scalable and efficient AI infrastructure.
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