AIBrix is a cloud-native infrastructure framework for large-scale LLM inference, designed to offer scalable and cost-efficient inference deployment. It includes routing, autoscaling, distributed inference, and KV caching components to build production-grade LLM services on Kubernetes.
Main Features
- High-density LoRA management and model adapters for lightweight adaptation and deployment.
- LLM gateway and routing for multi-model and multi-replica traffic management.
- Autoscaler tailored for inference workloads to dynamically scale resources and optimize costs.
Use Cases
- Enterprise LLM inference platform and service deployment.
- Mixed-model deployments with cost optimization requirements.
- Research and engineering scenarios for building and evaluating large-scale inference baselines.
Technical Highlights
- Implemented with Go and Python, designed for Kubernetes-native deployment.
- Supports distributed inference, distributed KV cache, and heterogeneous GPU scheduling to improve throughput and cost efficiency.
- Open source (Apache-2.0) with extensive documentation and community support.