Accelerating Financial Intelligence via High Throughput Distributed Systems for Large Language Model Augmented Time Series Forecasting
Keywords:
Distributed Systems, Financial Intelligence, Large Language Models, Time Series Forecasting, High-Throughput Inference, Socio-Technical Infrastructure, Algorithmic Governance.Abstract
The integration of Large Language Models (LLMs) into financial time series forecasting represents a paradigm shift from purely frequentist econometric models to context-aware reasoning systems. While traditional quantitative methods excel at identifying statistical patterns in numerical data, they often fail to capture the nuanced causal drivers found in unstructured textual narratives. LLM-augmented forecasting addresses this gap by synthesizing market microstructure signals with macroeconomic sentiment. However, the deployment of such models in high-frequency financial environments is hindered by the significant computational latency of transformer-based architectures. This paper proposes a high-throughput distributed system architecture specifically optimized for LLM-augmented financial intelligence. We investigate the structural trade-offs between model quantization, speculative decoding, and distributed inference across heterogeneous compute clusters. The proposed framework emphasizes system-level robustness, hardware-aware orchestration, and the socio-technical implications of automated financial decision-making. By aligning high-throughput engineering with advanced linguistic reasoning, the system enables real-time forecasting that remains resilient to market non-stationarity. Furthermore, we examine the governance requirements for these systems, focusing on algorithmic fairness, environmental sustainability, and the evolving regulatory landscape for autonomous financial agents. The discussion concludes with a forward-looking perspective on the role of distributed systems in achieving equitable and stable global financial markets.
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