ZeroCredits
ZeroCredits implements an adaptive inference layer that assigns each request to the most suitable local or remote model based on task type, complexity, latency, and expected output. Inspired by recent research on LLM routing and cascading, it combines lightweight local execution, specialized model selection, bounded concurrency, fallback controls, and response validation. The architecture reduces unnecessary compute while preserving quality for reasoning, extraction, summarization, and code workloads. Its modular routing policy can be calibrated as models, costs, and service objectives evolve, creating a resilient AI delivery layer optimized for speed, efficiency, governance, and consistent production performance across diverse workloads.
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