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Delentia OS: The Intent-Centric AI Operating System Architecture for Local Edge VRAM Optimization

Year: 2026
Type: preprint
Abstract: — The deployment of Large Language Models (LLMs) in enterprise environments is currently bottlenecked by extreme resource overhead, variable API pricing, and a lack of technological sovereignty. This paper presents Delentia OS, an intent-centric AI operating system architecture designed to run entirely on local edge consumer hardware. Driven by the JITNA (RFC-001) protocol and an underlying mathematical governance framework (F = D^I * A), Delentia OS utilizes a frozen 8-billion parameter base model coupled with a dynamic Low-Rank Adaptation (LoRA) swapping scheduler. Key Architectural Highlights: • Sub-12ms Dynamic LoRA Swapping: Hot-swaps four specialized cognitive adapters (Router, Guardian, Executor, Scribe) within local VRAM in under 12 milliseconds (actual avg. 11.2ms).• Differential Memory Retention (Delta Engine): Implements ALGO-41 to compress long-term semantic memory, reducing context retrieval VRAM footprint by 74.2% and lowering repeated query inference costs by up to 99.4%.• Deterministic Mathematical Governance: Regulates autonomous agentic workflows via the FDIA authorization gate check (F = D^I * A), guaranteeing absolute transaction-level safety. Empirical Verification & Benchmarks: • Zero-Crash Invariant Verification: Evaluated via property-based testing across 205,999 regression examples with zero software crashes (0.00% crash rate).• Absolute Execution Precision: Achieves a 0.00% syntax error rate in structured JSON generation and automated tool-calling workflows. Keywords— Intent-Centric AI, Edge Computing, Dynamic LoRA Swapping, VRAM Optimization, Cognitive Operating System, SignedAI Consensus, Technological Sovereignty, JITNA Protocol, Small Language Models (SLM).
DOI: 10.5281/zenodo.20920051
Language: English
Open Access status: green
OpenAlex ID: W7166140168
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