How vllm-project/aibrix works
AIBrix is cloud-native infrastructure for deploying, routing, and scaling LLM inference. A principal operator workflow is to submit a deployment through the console, have the control plane plan and provision resources, and expose a running model for inference. The runtime also includes autoscaling, model-adapter management, standardized engine metrics, batch dispatch, and distributed KV-cache capabilities. The graph combines README-documented capabilities with the sampled controller, batch, metrics, and runtime source; several broader subsystems are represented at their existing directory paths because their implementation was not sampled.
Main entry points: runtime Model Engines · handler Console API · src Console UI · impl Job Planner
Components 20
Management Console
- Console UI
apps/console/web/src - Console API
apps/console/api/handler - Job Planner
apps/console/api/planner/impl - Resource Manager
apps/console/api/resource_manager - Cloud Providers
apps/console/api/resource_manager/provider - Console Store
apps/console/api/store
Kubernetes Control
- Custom Resources
api - LoRA Controller
pkg/controller/modeladapter/modeladapter_controller.go - Pod Autoscaler
pkg/controller/podautoscaler/podautoscaler_controller.go
Inference Serving
- LLM Gateway
python/aibrix/aibrix/openai_frontend - Model Engines
python/aibrix/aibrix/runtime - Batch Dispatch
python/aibrix/aibrix/batch/client/engine.py - Batch Storage
python/aibrix/aibrix/batch/storage/batch_storage.py
KV Cache
- KV Cache Manager
python/aibrix_kvcache/aibrix_kvcache/cache_manager.py - Distributed KV Cache
pkg/cache/kvcache
Metrics and Scaling
- Engine Metrics
pkg/metrics/engine_fetcher.go - Metric Standardization
python/aibrix/aibrix/metrics/engine_rules.py - Autoscaling API
api/autoscaling/v1alpha1/podautoscaler_types.go
Other components
- Platform Operator
- Kubernetes
Connections 20
- Platform Operator → Console UI: submits deployment
- Console UI → Console API: calls API
- Console API → Console Store: persists records
- Console API → Job Planner: submits jobs
- Job Planner → Resource Manager: requests resources
- Resource Manager → Cloud Providers: selects provider
- Cloud Providers → Kubernetes: provisions workloads
- Kubernetes → Custom Resources: stores resources
- Kubernetes → LoRA Controller: reconciles adapters
- Kubernetes → Pod Autoscaler: reconciles scaling
- Platform Operator → LLM Gateway: sends inference
- LLM Gateway → Model Engines: routes requests
- Platform Operator → Batch Storage: supplies batch input
- Batch Storage → Batch Dispatch: reads requests
- Batch Dispatch → Model Engines: dispatches work
- Model Engines → KV Cache Manager: uses cache
- KV Cache Manager → Distributed KV Cache: reads and writes
- Model Engines → Engine Metrics: exposes metrics
- Engine Metrics → Metric Standardization: standardizes metrics
- Autoscaling API → Pod Autoscaler: configures scaling