Principle:InternLM Lmdeploy Load Balancing Proxy
| Knowledge Sources | |
|---|---|
| Domains | LLM_Serving, Infrastructure |
| Last Updated | 2026-02-07 15:00 GMT |
Overview
A request distribution mechanism that routes client requests across multiple LMDeploy API server instances with configurable load balancing and serving strategies.
Description
Load Balancing Proxy enables horizontal scaling of LLM serving by running multiple api_server instances behind a single proxy endpoint. Features include:
- Routing strategies: Random, minimum expected latency, minimum observed latency
- Serving strategies: Hybrid (colocation), DistServe (prefill-decode disaggregation)
- Health monitoring: Automatic node registration and deregistration
- API key authentication: Proxy-level key management
This is essential for production deployments where a single GPU node cannot handle the traffic volume or where different models need to be served from a unified endpoint.
Usage
Use this when scaling beyond a single api_server instance. Deploy multiple api_server nodes, then start the proxy to distribute requests. Useful for multi-model serving, high-availability setups, and prefill-decode disaggregation.
Theoretical Basis
Load balancing follows standard reverse proxy patterns:
# Abstract proxy routing
def route_request(request, nodes, strategy):
if strategy == 'random':
return random.choice(nodes)
elif strategy == 'min_expected_latency':
return min(nodes, key=lambda n: n.expected_latency())
elif strategy == 'min_observed_latency':
return min(nodes, key=lambda n: n.observed_latency())