Paddler is an open-source LLM infrastructure platform designed to deploy, manage, and scale large language models on private infrastructure. The system acts as a specialized load balancer and serving layer for language models, enabling organizations to run inference workloads without relying on external API providers. It supports running models locally through engines such as llama.cpp while distributing requests across multiple compute nodes to improve performance and reliability. The architecture is designed with privacy and cost control in mind, making it suitable for organizations that handle sensitive data or require predictable operational costs. Paddler also includes tools for monitoring, request buffering, and autoscaling integration so that deployments can adapt dynamically to changing workloads. A built-in administrative interface allows developers and operations teams to manage models, observe system performance, and test inference endpoints.
Features
- LLM-specific load balancing and inference routing
- Support for local model execution through llama.cpp integration
- Dynamic scaling through agent-based host registration
- Request buffering enabling scale-to-zero deployments
- Built-in web administration panel for monitoring and testing
- Observability metrics for tracking model performance and usage