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Vol. 12 No. 11 (2025)
International Multidisciplinary Journal for Research & Development

Articles

OPTIMIZING ATTENTION AND INFERENCE IN LARGE LANGUAGE MODELS: BALANCING EFFICIENCY, INTERPRETABILITY, AND ENERGY CONSUMPTION

Published 2025-11-27

  • Franz Schiller

Franz Schiller
TUM School of Computation, Information and Technology, Germany

Abstract

The rapid growth of large language models (LLMs) has intensified interest in the computational, energetic, and interpretive properties of attention mechanisms and supporting inference infrastructure. This article synthesizes theoretical and empirical insights across two intertwined research streams: the internal mechanics of attention in transformer models (comprehending the functional role and interpretability of multi-head and sparse attention) and systems-level approaches to efficient, low-latency, and energy-aware inference (including KV caches, heavy-hitter techniques, and firmware-level scheduling). We present a cohesive conceptual framework that reconciles apparent tensions—such as whether attention weights constitute explanations of model behavior and whether dense multi-head attention is uniformly necessary—by connecting representational redundancy to opportunities for structured sparsity and cache-aware inference. Building on prior analyses of attention distribution, heavy-hitter phenomena in token streams, and lifecycle energy accounting, we argue for an integrative approach: adaptive attention architectures that dynamically reallocate head resources, combined with inference-time KV cache management and scheduling policies that prioritize heavy-hitter contexts. We discuss methodological principles for evaluating such architectures—focusing on causal probing, ablation procedures, and realistic inference benchmarks that capture latency, throughput, and energy budgets. Limitations of extant studies are detailed, and we outline a roadmap for research blending model-centric and systems-centric innovation. Our synthesis highlights how careful co-design of attention mechanisms and inference systems can preserve or even enhance model fidelity while substantially reducing computational and environmental cost, offering concrete directions for both algorithmic research and practical deployment.

Keywords

Attention interpretability, sparse attention, heavy-hitter

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References

  1. Michel, P.; Levy, O.; Neubig, G. Are Sixteen Heads Really Better Than One? In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada, 8–14 December 2019. Available online: https://arxiv.org/abs/1905.10650 (accessed on 2 June 2025).
  2. Jain, S.; Wallace, B.C. Attention Is Not Explanation. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada, 8–14 December 2019. Available online: https://arxiv.org/abs/1902.10186 (accessed on 2 June 2025).
  3. Wiegreffe, S.; Pinter, Y. Attention is not not Explanation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Vancouver, BC, Canada, 8–14 December 2019. Available online: https://arxiv.org/abs/1908.04626 (accessed on 2 June 2025).
  4. Takase, S.; Okazaki, N. Sparse Attention with Linear Units. In Proceedings of the Association for Computational Linguistics (ACL), Online, 5–10 July 2020. Available online: https://arxiv.org/abs/2104.07012 (accessed on 2 June 2025).
  5. Clark, K.; Khandelwal, U.; Levy, O.; Manning, C.D. What Does BERT Look at? An Analysis of BERT’s Attention. In Proceedings of the BlackboxNLP Workshop at ACL, Florence, Italy, 1 August 2019. Available online: https://arxiv.org/abs/1906.04341 (accessed on 2 June 2025).
  6. Zhang, Z.; Sheng, Y.; Zhou, T.; Chen, T.; Zheng, L.; Cai, R.; Song, Z.; Tian, Y.; Ré, C.; Barrett, C.; et al. HO: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models. In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, 10–16 December 2023. Available online: https://arxiv.org/abs/2306.14048 (accessed on 2 June 2025).
  7. Xiao, G.; Tian, Y.; Chen, B.; Han, S.; Lewis, M. Efficient Streaming Language Models with Attention Sinks. In Proceedings of the ICLR, Vienna, Austria, 7–11 May 2024. Available online: https://arxiv.org/pdf/2309.17453 (accessed on 2 June 2025).
  8. Zhao, J.; Fang, Z.; Li, S.; Yang, S.; He, S. BUZZ: Beehive-structured sparse KV cache with segmented heavy hitters for efficient LLM inference. arXiv 2024, arXiv:2410.23079. Available online: https://arxiv.org/abs/2410.23079 (accessed on 2 June 2025).
  9. Chen, Y.; Wang, G.; Shang, J.; Cui, S.; Zhang, Z.; Liu, T.; Wang, S.; Yu, D.; Wu, H. NACL: A general and effective KV cache eviction framework for LLMs at inference time. arXiv 2024, arXiv:2408.03675. Available online: https://arxiv.org/abs/2408.03675 (accessed on 2 June 2025).
  10. Samsi, S.; Zhao, D.; McDonald, J.; Li, B.; Michaleas, A.; Jones, M.; Bergeron, W.; Kepner, J.; Tiwari, D.; Gadepally, V. From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference. 2023 IEEE High Performance Extreme Computing Conference (HPEC), Sep. 2023, pp. 1–9.
  11. Luccioni, S.; Jernite, Y.; Strubell, E. Power Hungry Processing: Watts Driving the Cost of AI Deployment? Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024, pp. 85–99.
  12. Berthelot, A.; Caron, E.; Jay, M.; Lefevre, L. Estimating the environmental impact of Generative-AI services using an LCA-based methodology. Procedia CIRP, vol. 122, pp. 707–712, 2024.
  13. Chandra, R. Reducing latency and enhancing accuracy in LLM inference through firmware-level optimization. International Journal of Signal Processing, Embedded Systems and VLSI Design, 5(2), 26-36, 2025.
  14. Coignion, T.; Quinton, C.; Rouvoy, R. Green My LLM: Studying the key factors affecting the energy consumption of code assistants. arXiv, Nov. 2024.
  15. Liu, J.; Xie, S.; Wang, J.; Wei, Y.; Ding, Y.; Zhang, L. Evaluating Language Models for Efficient Code Generation. arXiv, Aug. 2024.
  16. Garg, S.; Moghaddam, R. Z.; Sundaresan, N. RAPGen An Approach for Fixing Code Inefficiencies in Zero-Shot. arXiv, Jul. 2024.
  17. Gao, S.; Gao, C.; Gu, W.; Lyu, M. Search-Based LLMs for Code Optimization. arXiv, Aug. 2024.
  18. Shypula, A. G.; Madaan, A.; Zeng, Y.; Alon, U.; Gardner, J. R.; Yang, Y.; Hashemi, M.; Neubig, G.; Ranganathan, P.; Bastani, O.; Yazdanbakhsh, A. Learning Performance-Improving Code Edits. In The Twelfth International Conference on Learning Representations, Oct. 2023.
  19. Huang, D.; Zeng, G.; Dai, J.; Luo, M.; Weng, H.; Qing, Y.; Cui, H.; Guo, Z.; Zhang, J. M. SwiftCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning. arXiv, Mar. 2025.
  20. Kyuongmin Kim et al., The Effect of Scheduling and Preemption on the Efficiency of LLM Inference Serving, November 2024. https://www.researchgate.net/publication/385750103_The_Effect_of_Scheduling_and_Preemption_on_the_Efficiency_of_LLM_Inference_Serving

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