vllm-project/aibrix

AIBrix is an open-source infrastructure framework written in Go that provides essential building blocks for constructing scalable GenAI inference systems. The project is specifically designed to address enterprise needs in deploying, managing, and scaling large language model inference workloads in cloud-native environments.

The framework delivers a comprehensive set of infrastructure components tailored for LLM deployment. Key features include high-density LoRA management for lightweight model adaptations, an LLM gateway and routing system for efficiently directing traffic across multiple models and replicas, and an LLM app-tailored autoscaler that dynamically adjusts inference resources based on real-time demand. The unified AI runtime operates as a versatile sidecar component enabling metric standardization, model downloading, and management across the infrastructure. AIBrix supports distributed inference architecture to handle large workloads across multiple nodes and implements distributed KV cache functionality for high-capacity, cross-engine KV reuse, which is critical for efficient LLM serving.

Cost efficiency is a central design principle, with the framework offering cost-efficient heterogeneous serving capabilities that enable mixed GPU inference while maintaining service level objective guarantees. The system includes proactive GPU hardware failure detection to prevent infrastructure degradation. The project has evolved through multiple releases, with versions ranging from v0.1.0 released in November 2024 through v0.7.0 released in June 2026, demonstrating consistent development and feature expansion.

AIBrix has gained visibility in the broader infrastructure community, with the team delivering keynotes and presentations at major conferences including KubeCon North America 2025, KubeCon China 2025, and KubeCon EU 2025. The project was also featured at the ASPLOS 2025 workshop, positioning it as a significant contribution to system research in LLM inference. The framework is licensed under Apache 2.0 and maintains comprehensive documentation, a blog for release announcements, and an active developer community through Slack channels.