vllm-project/aibrix

What it solves

AIBrix provides the building blocks for creating scalable, enterprise-grade GenAI inference infrastructure. It addresses the challenges of deploying, managing, and scaling large language model (LLM) inference in cloud-native environments, specifically focusing on cost-efficiency and resource optimization.

How it works

AIBrix acts as a Kubernetes-native control plane for LLM inference (such as vLLM). It utilizes a cloud-native architecture to manage the lifecycle of models and their replicas. Key components include an LLM Gateway for intelligent routing, an app-tailored autoscaler for dynamic resource adjustment, and a unified AI runtime sidecar for metric standardization and model management.

Who it’s for

It is designed for enterprises and system researchers who need to deploy large-scale LLM inference workloads across multiple nodes and heterogeneous GPU hardware while maintaining service level objectives (SLOs).

Highlights

• LLM-Aware Routing: Efficiently directs traffic across multiple models and replicas via a dedicated gateway.
• Cost-Efficient Heterogeneous Serving: Supports mixed GPU inference to reduce operational costs.
• High-Density LoRA Management: Streamlined support for lightweight, low-rank adaptations of models.
• Distributed KV Cache: Enables high-capacity KV reuse across different inference engines.
• Resource Management: Includes an app-tailored autoscaler and proactive GPU hardware failure detection.