AIBrix: Towards Scalable, Cost-Effective Large Language Model Inference Infrastructure (2504.03648v1)

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.

AIBrix: Enhancing LLM Deployment

The paper introduces AIBrix, an innovative open-source framework tailored for optimizing large-scale deployment of LLMs in cloud environments. Given the rapid proliferation and commercial application of LLMs, such as those offered by OpenAI or Anthropic, businesses and researchers encounter challenges related to infrastructure scalability, inference efficiency, and cost-effectiveness. The AIBrix framework is developed to specifically address these concerns through its cloud-native infrastructure that integrates seamlessly with inference engines like vLLM.

Key Contributions

AIBrix distinguishes itself from conventional cloud-native infrastructure through several innovations:

• High-density LoRA Management: This feature significantly reduces inference costs by enabling dynamic Low-Rank Adaptation (LoRA) registration processes, thereby streamlining adapter management for fine-tuned models. It leverages Kubernetes mechanisms to optimize model discovery, enhancing the system’s scalability and resource utilization.
• LLM-Specific Autoscaling: AIBrix incorporates a novel autoscaling mechanism that leverages scenario-driven policies alongside optimizations such as sliding window metric aggregation. This feature ensures real-time metrics reporting and efficient resource allocation, thereby reducing latency and improving throughput.
• Advanced Routing Strategies: Through an LLM-aware API gateway extending Envoy Gateway's capabilities, AIBrix facilitates instance routing optimized by token patterns, computing overhead, and cache availability. This reduces latency and ensures efficient traffic management in diverse deployment scenarios.
• Unified AI Runtime: Facilitating a vendor-agnostic approach, AIBrix's runtime automates model handling, configures engines, and ensures observability. The GPU streaming loader component accelerates model loading, bypassing disk I/O bottlenecks.
• Distributed KV Cache Pool: By introducing a distributed KV cache system, AIBrix improves cross-engine KV reuse and efficiency, achieving significant performance gains in token throughput and inference latency compared to native caching methods.
• Hybrid Multi-Node Orchestration: Through integrating Kubernetes and Ray, AIBrix achieves a balance between resource management and distributed execution, supporting large-scale LLM deployments while maintaining flexibility in updating and scaling operations.
• SLO-driven GPU Optimizer: This component dynamically balances cost efficiency with service level objectives (SLOs), optimizing heterogeneous GPU utilization based on workload characteristics.
• Accelerator Diagnostic Tools: Tools for early detection and simulation of hardware failures are incorporated, improving system resilience and enabling proactive management of potential GPU issues.

Implications and Future Directions

The introduction of AIBrix highlights several theoretical and practical implications. Practically, it provides AI practitioners with a framework that significantly reduces costs and enhances LLM deployment capabilities across various industries. Theoretically, it pushes the boundaries of inference optimization by bridging sophisticated model enhancements with system-level orchestration.

The paper suggests that future developments in AIBrix could explore the refinement of profiling capabilities and adaptation to dynamic workloads. It also emphasizes streamlining profiling processes through lightweight analytical models, further bridging inference and infrastructure optimization.

Overall, AIBrix represents a significant advancement in the scalability and efficiency of LLM deployment infrastructure, offering a robust solution for researchers and enterprises requiring high-performance AI applications.