Fin-LLM-Inference: A High-Throughput Distributed System for Real-Time Financial Time Series Forecasting via Heterogeneous LLM-Augmented Reasoning Pipelines
Keywords:
Distributed Systems, Financial Time Series, Large Language Models, Heterogeneous Computing, High-Throughput Inference, Socio-Technical Infrastructure, Algorithmic GovernanceAbstract
The integration of Large Language Models (LLMs) into financial time series forecasting represents a transformative shift from purely frequentist econometric models to context-aware reasoning systems. However, the high-throughput requirements of modern capital markets create a significant tension with the computational latency inherent in transformer-based architectures. This paper introduces Fin-LLM-Inference, a high-throughput distributed system designed for real-time financial forecasting using heterogeneous LLM-augmented reasoning pipelines. We propose a multi-tiered architecture that strategically partitions reasoning tasks between optimized edge-based distilled models and robust cloud-based reasoning engines. By aligning hardware-aware optimizations with the unique non-stationarity of financial data, the system achieves a balance between predictive depth and execution speed. Our analysis focuses on the system-level trade-offs involving inference latency, model consistency, and architectural robustness. Furthermore, we examine the socio-technical implications of deploying such systems, including algorithmic governance, environmental sustainability in massive-scale AI clusters, and the policy challenges associated with automated financial decision-making. We argue that the future of financial intelligence lies in the seamless coordination of heterogeneous compute resources that can interpret both microstructure signals and macroeconomic narratives. This research provides a comprehensive blueprint for the next generation of resilient, scalable, and fair financial AI infrastructure, concluding with a forward-looking discussion on the regulatory landscape for autonomous financial agents.
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