AIBrix: Towards Scalable, Cost-Effective Large Language Model Inference Infrastructure

TL;DR

AIBrix tackles the challenge of cost-efficient, low-latency production-grade LLM inference by delivering a cloud-native, co-designed infrastructure that tightly couples inference engines with system orchestration. It introduces key innovations across a unified control/data-plane stack, including high-density LoRA management, LLM-aware routing, a unified AI runtime with a GPU streaming loader, a distributed KV cache, mix-grain multi-node orchestration, and a GPU-aware optimizer. Empirical results demonstrate substantial improvements, such as up to ~50% peak throughput gains and notable latency reductions, alongside metrics for heterogeneous GPU setups and reliability tooling for accelerator failures. The framework aims to enable scalable, vendor-agnostic LLM serving with automated multi-cluster scheduling and cost-aware resource management for real-world deployments.

Abstract

We introduce AIBrix, a cloud-native, open-source framework designed to optimize and simplify large-scale LLM deployment in cloud environments. Unlike traditional cloud-native stacks, AIBrix follows a co-design philosophy, ensuring every layer of the infrastructure is purpose-built for seamless integration with inference engines like vLLM. AIBrix introduces several key innovations to reduce inference costs and enhance performance including high-density LoRA management for dynamic adapter scheduling, LLM-specific autoscalers, and prefix-aware, load-aware routing. To further improve efficiency, AIBrix incorporates a distributed KV cache, boosting token reuse across nodes, leading to a 50% increase in throughput and a 70% reduction in inference latency. AIBrix also supports unified AI runtime which streamlines model management while maintaining vendor-agnostic engine compatibility. For large-scale multi-node inference, AIBrix employs hybrid orchestration -- leveraging Kubernetes for coarse-grained scheduling and Ray for fine-grained execution -- to balance efficiency and flexibility. Additionally, an SLO-driven GPU optimizer dynamically adjusts resource allocations, optimizing heterogeneous serving to maximize cost efficiency while maintaining service guarantees. Finally, AIBrix enhances system reliability with AI accelerator diagnostic tools, enabling automated failure detection and mock-up testing to improve fault resilience. AIBrix is available at https://github.com/vllm-project/aibrix.

Figures (9)

• Figure 1: AIBrix Architecture Overview.
• Figure 2: High-Density LoRA Management.
• Figure 3: Advanced LLM Gateway and Routing.
• Figure 4: AIBrix's unified AI runtime
• Figure 5: Distributed KV Cache Pool.