The rapid proliferation of large-scale artificial intelligence (AI) models across domains—including natural language processing, computer vision, and multimodal integration—has intensified the need for standardized modularization at the systems level. Present AI deployment practices are dominated by monolithic architectures or ad-hoc integration strategies, which hinder portability, reproducibility, and maintainability. While partial standards such as ONNX, MLIR, and vendor-specific inference formats have emerged, these frameworks lack a unified, complete specification for the modular decomposition of AI components across the model lifecycle.

This work introduces the Standardized Modularization Framework for AI (SMF-AI), the first comprehensive, systems-level standard explicitly designed to define interoperable module boundaries for AI systems. SMF-AI specifies five core, independently swappable modules:

(1) Model Architecture Specification (MAS),

(2) Model Parameter Set (MPS),

(3) Inference Engine (IE),

(4) Preprocessing & Postprocessing Modules (PPM),

(5) System Orchestration Layer (SOL).

The framework prescribes interface schemas, metadata standards, and cryptographic integrity verification mechanisms to ensure cross-platform compatibility and lifecycle independence. By aligning design principles with proven modular paradigms from networking (e.g., OSI model) and computing (e.g., ISA–level hardware abstraction), SMF-AI facilitates reproducible research, vendor-neutral deployment, and sustainable AI ecosystem growth.

Our contributions include:

(i) formal definition of the SMF-AI specification,

(ii) reference schema implementations,

(iii) compatibility and compliance strategies for existing models,

(iv) evaluation through multi-engine, multi-parameter deployment case studies.

This work lays the foundation for future AI component marketplaces, automated compliance tooling, and a globally interoperable AI software stack.