Research Collaboration#
At AIBrix, we strongly support system-level research and are committed to fostering collaboration with researchers and academics in the AI infrastructure domain. Our platform provides a unique opportunity to bridge the gap between theoretical research and real-world production challenges. If you’re a PhD student or researcher looking to explore innovative ideas in AI systems, we’d love to support your work.
Opportunities for Research#
Unresolved Production Challenges: The AI production environment presents numerous unresolved problems, from efficient resource allocation to scalable inference architectures. We can provide case studies and real-world scenarios for researchers interested in exploring and solving these challenges.
Research Paper Implementation: Some research ideas have been integrated into AIBrix, making it a practical landing ground for cutting-edge innovations. We regularly publish Help Wanted issues on our GitHub, highlighting open research opportunities—feel free to take a look and contribute!
AIBrix as a Research Testbed: Our system is designed to serve as a research testbed for system-level problems in AI infrastructure. Whether it’s testing new scheduling algorithms, optimizing inference latency, or improving resource efficiency, we provide hands-on support to help set up experiments and validate hypotheses.
Acknowledgments#
Many of our innovative ideas have been inspired by academic research, including works such as Preble, Melange, QLM, and MoonCake. 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 also extend our gratitude to 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.
Get Involved#
At AIBrix, we actively welcome research collaborations across a wide range of topics, from cloud infrastructure cost optimizations to engine and system co-design innovations. AIBrix offers a robust experimentation platform featuring:
Request Routing Strategies
LLM Specific Autoscaling
Disaggregated KV Cache Pool
Serverless & Engine Resource Elasticity
Large scale inference system tracing and simulation
Whether you’re a researcher, an academic, or an engineer working on GenAI system optimization strategies, we invite you to collaborate with us and contribute to the future of scalable AI infrastructure.
For inquiries or collaboration opportunities, feel free to reach out to us by cutting Github issues or through the Slack Channel.