Efficient Model Compression and Quantization Techniques for Large Language Model Deployment
Keywords:
large language models, model compression, quantization, deployment systems, governance, sustainability, fairness, inference infrastructureAbstract
Large language models have become a central component of contemporary artificial intelligence infrastructure, yet their deployment remains constrained by memory bandwidth, latency, energy consumption, hardware availability, and cost. This paper examines efficient model compression and quantization techniques as socio-technical deployment strategies rather than isolated algorithmic optimizations. It analyzes how quantization, pruning, knowledge distillation, low-rank factorization, and hybrid compression pipelines alter the operational envelope of large language models across cloud, edge, and regulated environments. Particular attention is given to structural trade-offs between accuracy, robustness, fairness, interpretability, and infrastructure efficiency. The discussion emphasizes that compression decisions are not merely technical choices; they shape access, accountability, sustainability, and the distribution of computational power. Through a system-level review, the paper considers deployment architectures, inference serving, governance requirements, and policy implications. It argues that effective deployment requires coordinated advances in hardware-aware algorithms, evaluation protocols, documentation practices, and lifecycle governance. The conclusion proposes a research agenda centered on adaptive compression, standardized robustness auditing, transparent model reporting, and sustainable infrastructure planning. The central claim is that model compression and quantization must be understood as governance-relevant components of the machine learning stack, because they determine which models can be deployed, under what constraints, and with what social consequences.
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