Towards High-Throughput Financial Intelligence: A Hardware-Aware Distributed Infrastructure for Real-Time Time Series Forecasting via Speculative LLM Decoding
DOI:
https://doi.org/10.66280/cis.v1i1.256Keywords:
Distributed Systems, Financial Forecasting, Speculative Decoding, Large Language Models, Hardware-Aware Computing, High-Frequency Trading, Socio-Technical InfrastructureAbstract
The digital transformation of global capital markets has necessitated a transition from traditional autoregressive forecasting models to sophisticated architectures capable of synthesizing high-frequency market microstructure with qualitative narrative semantics. This paper proposes a hardware-aware distributed infrastructure designed for real-time financial time series forecasting, leveraging the emerging paradigm of speculative Large Language Model (LLM) decoding. We argue that traditional financial intelligence systems suffer from an architectural bottleneck where the latency of autoregressive token generation in LLMs conflicts with the millisecond-level requirements of high-frequency trading environments. Our proposed framework addresses this through a tiered distributed system that offloads initial predictive drafting to lightweight, hardware-optimized edge models, which are subsequently verified or corrected by a robust cloud-based LLM. We provide a comprehensive system-level analysis of this infrastructure, emphasizing the structural trade-offs between computational throughput, inference latency, and predictive accuracy. The discussion extends into the socio-technical dimensions of deployment, including the governance of autonomous financial agents, the environmental sustainability of large-scale GPU clusters, and the policy implications for market stability and algorithmic fairness. By integrating hardware-specific optimizations with a speculative decoding orchestration layer, our framework offers a scalable blueprint for the next generation of resilient financial AI infrastructure. This research concludes with a forward-looking perspective on the ethics of automated reasoning in global finance and the evolving regulatory landscape surrounding high-throughput AI deployment.
References
1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308-318.
2. Anati, I., Gueron, S., Johnson, S., & Scarlata, V. (2013). Innovative instructions and software model for isolated execution. Proceedings of the 2nd International Workshop on Hardware and Architectural Support for Security and Privacy, 10(1).
3. Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., ... & Roselander, J. (2019). Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046.
4. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
5. Cartea, A., Jaimungal, S., & Penalva, J. (2015). Algorithmic and High-Frequency Trading. Cambridge University Press.
6. Chen, Y., & Sun, Y. (2020). Social commerce: A systematic review and future research directions. Journal of Business Research, 111, 1-10.
7. Costan, V., & Devadas, S. (2016). Intel SGX explained. Cryptology ePrint Archive.
8. Chen, X. (2024, November). Cloud Storage User Behavior Analysis and Dynamic Replica Strategy Optimization Based on Improved RFM and Fuzzy Clustering. In International Conference on Cognitive based Information Processing and Applications (pp. 425-434). Singapore: Springer Nature Singapore.
9. Dwork, C. (2008). Differential privacy: A survey of results. International Conference on Theory and Applications of Models of Computation, 1-19.
10. Ghoshal, B., & Tucker, A. (2022). Scalable inference for deep learning in finance. Quantitative Finance, 22(10), 1845-1860.
11. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
12. Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29.
13. Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), 1-33.
14. Kaplan, J., et al. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
15. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1-2), 1-210.
16. Kirilenko, A. S., et al. (2017). The Flash Crash: High-frequency trading in an electronic market. The Journal of Finance, 72(3), 967-998.
17. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60.
18. Liu, T. (2026). A Comparative Study of Transformer-Based and Classical Models for Financial Time-Series Forecasting. Journal of Risk and Financial Management, 19(3), 203.
19. Lo, A. W. (2017). Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press.
20. Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
21. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273-1282.
22. Mo, F., Haddadi, H., Katiyar, K., Ansari, R., & Chuah, C. N. (2021). PPFL: Privacy-preserving federated learning with trusted execution environments. Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, 94-108.
23. Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. 2008 IEEE Symposium on Security and Privacy, 111-125.
24. Nisan, N., Roughgarden, T., Tardos, E., & Vazirani, V. V. (2007). Algorithmic Game Theory. Cambridge University Press.
25. Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
26. Shalf, J. (2020). The future of computing beyond Moore’s Law. Philosophical Transactions of the Royal Society A, 378(2166).
27. Stoica, I., et al. (2017). Ray: A distributed framework for emerging AI applications. 13th USENIX Symposium on Operating Systems Design and Implementation.
28. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
29. Wu, S., et al. (2023). BloombergGPT: A large language model for finance. arXiv preprint arXiv:2303.17564.
30. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.
31. Zaharia, M., et al. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. 9th USENIX Symposium on Networked Systems Design and Implementation.
32. Zhang, L., et al. (2021). Deep reinforcement learning for automated stock trading: An ensemble strategy. SSRN Electronic Journal.
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