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SplitLLM is a production-inspired hybrid AI routing platform designed to make large language model deployments faster, more cost-efficient, and fully explainable. Instead of sending every request to a single cloud model, SplitLLM first analyzes each prompt using a multi-stage reasoning pipeline to determine its complexity, confidence requirements, expected cost, and execution strategy. Based on this analysis, it intelligently routes requests to either lightweight local models or powerful cloud models, ensuring the right model is used for the right task. Unlike traditional routers that only decide where to send a request, SplitLLM explains why every routing decision was made. Every prediction includes transparent reasoning, confidence estimates, routing factors, telemetry, and performance metrics, enabling users and organizations to understand and trust the system's decisions. The platform includes modular routing, configurable budget policies, live telemetry, benchmarking tools, a reasoning profiler, CLI utilities, REST APIs, Docker support, automated testing, and CI/CD workflows. It is designed with production engineering principles, making it easy to deploy, monitor, and extend. Built with AMD's AI ecosystem in mind, SplitLLM supports efficient local inference workflows and hybrid deployment strategies that reduce unnecessary cloud usage while maximizing available compute resources. The architecture is modular, scalable, and optimized for future integration with additional models and routing strategies. Our vision is simple: AI should not always use the biggest model—it should use the smartest one. SplitLLM delivers intelligent, explainable, and efficient AI routing for modern applications while helping developers reduce operational costs, improve response times, and maintain high-quality outputs.

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