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High-performance Rust-based load balancer for VLLM with multiple routing algorithms and prefill-decode disaggregation support

Project description

vLLM Router

A high-performance and light-weight request forwarding system for vLLM large scale deployments, providing advanced load balancing methods and prefill/decode disaggregation support.

Key Features

  • Core Architecture: Request routing framework and async processing patterns
  • Load Balancing: Multiple algorithms (cache-aware, power of two, consistent hashing, random, round robin)
  • Prefill-Decode Disaggregation: Specialized routing for separated processing phases
  • Service Discovery: Kubernetes-native worker management and health monitoring
  • Enterprise Features: Circuit breakers, retry logic, metrics collection

Quick Start

Prerequisites

Rust and Cargo:

# Install rustup (Rust installer and version manager)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Follow the installation prompts, then reload your shell
source $HOME/.cargo/env

# Verify installation
rustc --version
cargo --version

# Install protobuf compiler (on Ubuntu/Debian)
sudo apt-get update
sudo apt-get install -y protobuf-compiler libprotobuf-dev

Python with pip installed

Installation & Basic Usage

Rust Binary

# Build Rust components
cargo build --release

Python Package

Install from PyPI

pip install vllm-router                                                                                                                                                        ```

To build from source:
```bash    
pip install setuptools-rust wheel build
python -m build
pip install dist/*.whl

# Rebuild & reinstall in one step during development
python -m build && pip install --force-reinstall dist/*.whl

Usage Examples

Standard Data Parallelism Routing

# Launch router with data parallelism (8 replicas per worker URL)
# When data-parallel-size > 1, the router automatically creates DP-aware workers
./target/release/vllm-router \
    --worker-urls http://worker1:8000 http://worker2:8000 \
    --policy consistent_hash \
    --intra-node-data-parallel-size 8

# Alternative: using cargo run
cargo run --release -- \
    --worker-urls http://worker1:8000 http://worker2:8000 \
    --policy consistent_hash \
    --intra-node-data-parallel-size 8

# Alternative: using python launcher
vllm-router \
  --worker-urls http://worker1:8000 http://worker2:8000 \
    --policy consistent_hash \
    --intra-node-data-parallel-size 8

Prefill-Decode Disaggregation

# When vLLM runs the NIXL connector, prefill/decode URLs are required.
# See a working example in scripts/llama3.1/ folder.
cargo run --release -- \
    --policy consistent_hash \
    --vllm-pd-disaggregation \
    --prefill http://127.0.0.1:8081 \
    --prefill http://127.0.0.1:8082 \
    --decode http://127.0.0.1:8083 \
    --decode http://127.0.0.1:8084 \
    --decode http://127.0.0.1:8085 \
    --decode http://127.0.0.1:8086 \
    --host 127.0.0.1 \
    --port 8090 \
    --intra-node-data-parallel-size 1 \


# When vLLM runs the NCCL connector, ZMQ based discovery is supported.
# See a working example in scripts/install.sh
cargo run --release -- \
    --policy consistent_hash \
    --vllm-pd-disaggregation \
    --vllm-discovery-address 0.0.0.0:30001 \
    --host 0.0.0.0 \
    --port 10001 \
    --prefill-policy consistent_hash \
    --decode-policy consistent_hash

Configuration

Authentication

Enable bearer-token validation by listing validation URLs (comma-separated) in .env via API_KEY_VALIDATION_URLS or passing --api-key-validation-urls. When set, all HTTP endpoints require Authorization: Bearer <token> and tokens are validated with HTTP 200 responses.

# .env
API_KEY_VALIDATION_URLS=https://codebase.helmholtz.cloud/api/v4/user

# CLI override
vllm-router --api-key-validation-urls https://codebase.helmholtz.cloud/api/v4/user

Metrics

Prometheus metrics endpoint available at 127.0.0.1:29000 by default.

# Custom metrics configuration
vllm-router \
    --worker-urls http://localhost:8080 http://localhost:8081 \
    --prometheus-host 0.0.0.0 \
    --prometheus-port 9000

Retries and Circuit Breakers

Retry Configuration

Retries are enabled by default with exponential backoff and jitter:

vllm-router \
  --worker-urls http://localhost:8080 http://localhost:8081 \
  --retry-max-retries 3 \
  --retry-initial-backoff-ms 100 \
  --retry-max-backoff-ms 10000 \
  --retry-backoff-multiplier 2.0 \
  --retry-jitter-factor 0.1

Circuit Breaker Configuration

Circuit breakers protect workers and provide automatic recovery:

vllm-router \
  --worker-urls http://localhost:8080 http://localhost:8081 \
  --cb-failure-threshold 5 \
  --cb-success-threshold 2 \
  --cb-timeout-duration-secs 30 \
  --cb-window-duration-secs 60

Circuit Breaker State Machine:

  • ClosedOpen after N consecutive failures (failure-threshold)
  • OpenHalfOpen after timeout (timeout-duration-secs)
  • HalfOpenClosed after M consecutive successes (success-threshold)

Retry Policy: Retries on HTTP status codes 408/429/500/502/503/504, with backoff/jitter between attempts.

Request ID Tracking

Track requests across distributed systems with configurable headers:

# Use custom request ID headers
vllm-router \
    --worker-urls http://localhost:8080 \
    --request-id-headers x-trace-id x-request-id

Default headers: x-request-id, x-correlation-id, x-trace-id, request-id

Load Balancing Policies

The router supports multiple load balancing policies:

Policy Description Session Affinity Use Case
round_robin Sequential distribution across workers No General purpose, even distribution
random Uniform random selection No Simple deployments
consistent_hash Routes same session/user to same worker Yes Multi-turn chat, KV cache reuse
power_of_two Picks least loaded of two random workers No Load-sensitive workloads
cache_aware Optimizes for prefix cache hits Yes Repeated prompts, few-shot
# Example: Using consistent_hash with HTTP header for session affinity
curl -X POST http://router:8000/v1/chat/completions \
  -H "X-Session-ID: my-session-123" \
  -H "Content-Type: application/json" \
  -d '{"model": "llama-3", "messages": [{"role": "user", "content": "Hello!"}]}'

For detailed configuration options, hash key priorities, and usage examples, see Load Balancing Documentation.

Advanced Features

Kubernetes Service Discovery

Automatic worker discovery and management in Kubernetes environments.

Basic Service Discovery

vllm-router \
    --service-discovery \
    --selector app=vllm-worker role=inference \
    --service-discovery-namespace default

Command Line Arguments Reference

Service Discovery

  • --service-discovery: Enable Kubernetes service discovery
  • --service-discovery-port: Port for worker URLs (default: 8000)
  • --service-discovery-namespace: Kubernetes namespace to watch
  • --selector: Label selectors for regular mode (format: key1=value1 key2=value2)

Development

Troubleshooting

VSCode Rust Analyzer Issues: Set rust-analyzer.linkedProjects to the absolute path of Cargo.toml:

{
  "rust-analyzer.linkedProjects": ["/workspaces/vllm/vllm-router/Cargo.toml"]
}

CI/CD Pipeline

The continuous integration pipeline includes comprehensive testing, benchmarking, and publishing:

Build & Test

  1. Build Wheels: Uses cibuildwheel for manylinux x86_64 packages
  2. Build Source Distribution: Creates source distribution for pip fallback
  3. Rust HTTP Server Benchmarking: Performance testing of router overhead
  4. Basic Inference Testing: End-to-end validation through the router
  5. PD Disaggregation Testing: Benchmark and sanity checks for prefill-decode load balancing

Publishing

  • PyPI Publishing: Wheels and source distributions published when version changes in pyproject.toml
  • Container Images: Docker images published using /docker/Dockerfile.router

Acknowledgement

This project is a fork of SGLang Model Gateway, and we would like to explicitly acknowledge and thank the original authors for their work. At this stage, our fork includes only minimal changes to preserve the existing interface and ensure compatibility with vLLM. We anticipate further divergence as we pursue the roadmap we have in mind, which is the reason for creating the fork.

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