"The future belongs to those who believe in the beauty of their dreams." — Eleanor Roosevelt
Optimize compute efficiency and accelerate inference performance.
Multi-source model downloads– Support fetching models from diverse repositories.Model preloading– Reduce latency by preloading frequently used models.Near-compute-node acceleration– Improve the performance of loading models.
One-click deployment for simplicity and speed.
Multi-runtime support– Compatible with Dynamo, vLLM, and SGLang.Distributed inference–- Multi-node inference (LeaderWorkerSet)
- PD-separated inference (Dynamo)
Heterogeneous inference service deployment– Run across different hardware types.Extended parameter support– Flexible runtime optimization.SLO-based autoscaling– Dynamically adjust resources based on service-level objectives.
Intelligent routing and multi-tenant control.
Multi-model load balancing– Efficiently distribute inference requests.Model service discovery– Automatically detect and route to available instances.Multi-tenant management–- Model-level access control
- Model-level quota management
- Model-level request rate limiting
Tenant domain & certificate management– Secure and isolate tenant traffic.External SLB support– Integrate with external load balancers.
Prometheus integration– Collect and visualize key metrics.
Centralized control for administrators.
Multi-tenant management– Assign roles, permissions, and resources.User management– Token-based authentication & quota enforcement.Visual dashboard– Monitor system health and performance at a glance.
This roadmap outlines our vision for ARKS—a scalable, efficient, and user-friendly platform for AI model deployment and management. 🚀