Research & Industry Collaboration Invitation #699
Description
🚀 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