Optimizing Large Language Model Inference: Strategies for Latency Reduction, Energy Efficiency, and Cybersecurity Applications - Alexander Müller (Academic Reseach Library for International Journal of Computer Science & Information System, 2025)
Large Language Models (LLMs) have demonstrated transformative capabilities across natural language understanding, generation, and reasoning tasks. However, the deployment of LLMs at scale presents significant challenges in terms of inference latency, energy consumption, and effective integration within cybersecurity and telecommunications applications. This research comprehensively examines state-of-the-art strategies for optimizing LLM inference, focusing on caching mechanisms, heavy-hitter prioritization, streaming architectures, and firmware-level enhancements. Methods such as the Heavy-Hitter Oracle (HO), attention sinks, BUZZ sparse key-value caches, and the NACL eviction framework are analyzed for their impact on reducing computation overhead while preserving model accuracy (Zhang et al., 2023; Xiao et al., 2024; Zhao et al., 2024; Chen et al., 2024). Furthermore, energy benchmarking studies highlight the correlation between architectural efficiency and sustainability metrics, emphasizing the importance of low-power inference strategies (Samsi et al., 2023; Luccioni et al., 2024). The paper also investigates the application of LLMs in cybersecurity for adaptive intrusion detection, privacy-preserving threat analysis, and automated software testing, discussing how optimized inference directly contributes to the effectiveness and responsiveness of these systems (Lira et al., 2024; Ferrag et al., 2024; Wang et al., 2024). Through a detailed theoretical and practical examination of these methods, the study identifies current limitations, explores avenues for future research, and proposes an integrated framework that balances efficiency, scalability, and security considerations. The findings are essential for researchers, practitioners, and policymakers aiming to harness LLMs in high-stakes, resource-constrained environments.