Load Balancing Multiple Models

Distribute requests across multiple LLM providers and models for 99.99% uptime. Implement intelligent routing, failover, and cost optimization.

Intelligent Load Balancer

class ModelLoadBalancer:
    def __init__(self):
        self.models = {
            'primary': {'endpoint': 'gpt-4', 'weight': 0.5, 'latency': 200},
            'secondary': {'endpoint': 'claude-3', 'weight': 0.3, 'latency': 150},
            'fallback': {'endpoint': 'llama-3', 'weight': 0.2, 'latency': 100}
        }
        
    async def route_request(self, request):
        # Health check all endpoints
        healthy_models = await self.health_check()
        
        # Route based on latency requirements
        if request.max_latency < 150:
            return self.route_to_fastest(healthy_models)
        
        # Weighted round-robin for normal requests
        return self.weighted_route(healthy_models)

ParrotRouter handles load balancing automatically across 100+ models with built-in health checks and failover.

References

[1] arXiv. "Efficient LLM Inference" (2024)
[2] Hugging Face. "GPU Inference Optimization" (2024)
[3] NVIDIA. "LLM Inference Optimization" (2024)