Puffer Os: An Open-Weight Multi-LLM Orchestration Operating System

PUFFER OS: An Open-Weight Multi-LLM Orchestration Operating System introduces a systems architecture for orchestrating multiple open-weight large language models (LLMs) as a unified intelligent runtime rather than relying on a single model. The work presents Puffer OS, an operating-system-inspired orchestration framework that dynamically plans, routes, coordinates, verifies, and synthesizes responses across heterogeneous AI models based on task requirements, latency, cost, and reliability. Unlike traditional multi-agent pipelines or static mixture-of-experts approaches, Puffer OS provides a kernel-style control plane with explicit system calls, capability-based routing, typed task graphs, isolated memory, verification gates, and multi-objective scheduling. The architecture supports two execution modes: Puffer Kernel for low-latency interactive inference and Puffer Deep for complex multi-step reasoning using executable task graphs with parallel execution and verification loops. The framework incorporates Capability Cards for dynamic model profiling, a multi-objective routing function balancing quality, latency, cost, and risk, anti-collapse memory isolation between collaborating agents, offline evolutionary optimization using separable CMA-ES, and an extensible runtime capable of hot-plugging new open-weight models without retraining. The paper also specifies the orchestration kernel ABI, execution pipeline, learning control plane, security model, deployment architecture, and evaluation methodology for production-scale AI systems. Puffer OS is designed for enterprise, on-premises, and air-gapped deployments where transparency, reproducibility, and operational control are critical. By treating AI orchestration as an operating system rather than a prompt-engineering workflow, the work provides a foundation for scalable, auditable, and cost-efficient collective intelligence built entirely on open-weight models.