Memory-Efficient Fine-Tuning of Large Language Models for Enterprise Knowledge Automation
Keywords:
Large language models, memory-efficient fine-tuning, enterprise knowledge automation, parameter-efficient adaptation, model governance, sustainable AI, retrieval-augmented generationAbstract
Large language models have demonstrated remarkable capabilities in natural language understanding and generation, yet their prodigious memory and computational demands pose substantial barriers to cost-effective deployment in enterprise knowledge automation systems. This paper presents a comprehensive examination of memory-efficient fine-tuning strategies that enable organizations to adapt pre-trained language models to domain-specific knowledge bases while maintaining acceptable performance and operational feasibility. We systematically analyze parameter-efficient techniques, including low-rank adaptation, adapter modules, and prefix tuning, and evaluate their trade-offs in terms of memory footprint, training throughput, inference latency, and model fidelity. Beyond algorithmic considerations, we address the socio-technical dimensions of enterprise adoption, including governance frameworks for model versioning and compliance, robustness under distribution shift, fairness across diverse knowledge corpora, and the sustainability implications of reduced computational resource consumption. Architectural decisions for integrating fine-tuned models with existing enterprise data pipelines, retrieval-augmented generation, and knowledge graph infrastructures are discussed in depth. Through a synthesis of recent advances in sparse fine-tuning, quantization-aware training, and memory-optimized hardware utilization, we propose a layered deployment architecture that balances accuracy, cost, and regulatory constraints. The paper concludes with forward-looking recommendations for policy development and infrastructure design that align memory-efficient fine-tuning with the long-term goals of responsible and scalable enterprise automation.
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