vllm-project
Research & Industry Collaboration Invitation
🚀 Feature Description and Motivation
We're looking for contributors and collaborators to join efforts in pushing forward AI infrastructure research and industry adoption. If you're interested in LLM infrastructure, optimization, and scaling, this is a great opportunity to get involved!
🛠 Areas of Contribution We welcome contributions in the following areas:
- LLM Inference Optimization – Efficient model hosting, cost-effective inference, and high-performance scheduling.
- LoRA & Multi-LoRA Deployment – High-density deployment, dynamic model loading, and scaling strategies.
- Heterogeneous GPU Scheduling – Optimizing inference across diverse GPU types for cost and performance trade-offs.
- LLM Routing & Autoscaling – Traffic-aware routing, adaptive autoscaling, and stability improvements.
- Distributed Cache & Prefix Cache Improvements – Remote KV-backed solutions for better memory efficiency.
- Cloud-Native AI Runtime & Orchestration – Kubernetes-native AI workloads, serverless inference, and auto-scaling optimizations.
💡 How You Can Contribute ✅ Open issues & discuss new ideas ✅ Submit PRs to improve the project ✅ Share research insights or industry use cases ✅ Collaborate on benchmarks & performance evaluations ✅ Help with documentation and tutorials
📬 Get in Touch If you’re interested, feel free to:
- Comment below 👇
- Open a discussion
- Reach out via maintainer's email
Looking forward to collaborating with researchers, engineers, and AI infrastructure enthusiasts! Let’s build scalable, efficient, and cost-effective AI systems together. 🚀
🔥 Join the journey! 🔥
Use Case
N/A
Proposed Solution
No response
We happened to seek a solution to host and boost our business with MODEL serving, aibrix seems a lightweight and doable way, will be happy to contribute!
Collaborate on benchmarks & performance evaluations
@Jeffwan do we have/get shared resources like GKE, EKS, or AKS, to work on benchmarks or performance? Can't run benchmarks on personal cloud - it will be too expensive to run these and submit contributions..
