vllm-project/aibrix
Aibrix
Aibrix is an inference orchestrator designed for scaling, routing, and managing the deployment of large language models across distributed vLLM clusters. It serves as a centralized gateway for load balancing and routing traffic to specific model replicas and versions.
The system manages resource efficiency through a GPU cluster autoscaler that adjusts compute instance counts based on real-time request volume. It further optimizes operations by mixing different accelerator types within a single cluster and utilizing a model adapter orchestrator to deploy lightweight parameter adapters on shared base models.
Broad capabilities include the use of a distributed key-value cache manager to share token data across inference engines and the implementation of hardware health monitoring to detect processing unit failures. The project also provides a unified metrics pipeline to standardize performance data collection across diverse runtime environments.
Features
- Inference Gateways - Provides a centralized gateway that routes incoming inference requests to specific model replicas and versions for load balancing.
- Inference Deployment Orchestrators - Provides a system for managing the scaling and deployment of LLM inference workloads across distributed vLLM clusters.
- Adapter-Based Model Deployment - Implements a system for deploying lightweight parameter adapters on shared base models to optimize hardware resource usage.
- Traffic Management - Directs requests across multiple models and replicas through a centralized gateway to balance load.
- Inference Traffic Routing - Directs inference requests across multiple model replicas and versions through a centralized gateway to balance load.
- Automatic Inference Workload Scaling - Automatically adjusts the number of inference serving replicas based on real-time demand and request volume.
- Large Language Model Serving - Spreads massive model weights and computation across multiple hardware nodes to handle high request volumes.
- Auto Scaling - Adjusts compute instance counts in real time based on request volume to maintain target response times.
- Adapter Management - Manages the dynamic loading and serving of lightweight adapters to run multiple model variants on shared hardware.
- Distributed Model Parallelism - Spreads large model weights and computation across multiple hardware nodes to handle massive parameter counts.
- Heterogeneous Hardware Scaling - Mixes different GPU and accelerator types within a single cluster to optimize operational costs and service levels.
- KV Cache Management - Shares token data across different inference engines using a distributed key-value cache to increase capacity.
- Inference Cost Optimizers - Optimizes operational costs by mixing different hardware types for inference while meeting service level objectives.
- Multi-GPU Workload Distribution - Distributes computationally heavy LLM workloads across multiple GPU nodes to increase total throughput.
- LLM KV Cache Stores - Utilizes a distributed key-value cache manager to share token data across inference engines and increase throughput.
- GPU Resource Optimization - Optimizes operational costs by mixing different accelerator types and monitoring hardware health within a single cluster.
- Cluster Autoscalers - Adjusts compute instance counts in real time based on request volume and response time targets.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
