Mobile payments and high-frequency trading generate tens of millions of business requests per second. When running AI models such as anti-fraud and credit scoring, traditional CPU architectures suffer from high inference latency and insufficient throughput, making it impossible to complete risk decisions within millisecond-level time windows—directly endangering fund security and customer experience.
SOLUTION INTRODUCTION
Solution introduction
Delivers ultimate inference performance, elastic resource provisioning, and financial-grade high availability to help financial institutions achieve millisecond-level decision-making, intelligent compliance, and a fully upgraded customer experience—making AI a real-time productivity engine for business growth.Quantitative trading and ultra-fast market data require deterministic microsecond-level low latency. Any latency increase caused by server performance jitter or I/O congestion can lead to significant spread losses. At the same time, systems must meet “two locations, three centers” disaster recovery requirements and ensure 24/7 business continuity. Traditional architectures struggle to deliver both extreme speed and financial-grade high availability.
When building intelligent marketing, intelligent customer service, document review, and similar applications, financial institutions commonly face computing resource contention, complex training environment deployment, and long cycles from model development to production. The lack of a unified AI development platform and elastic computing support makes it difficult to quickly turn intelligent innovation into productivity.
SOLUTION ADVANTAGES
Solution advantages
The following points are replaceable demonstration content based on the current solution structure.Ultimate AI inference performance for real-time decision-making
Using AI servers’ unique inference acceleration engines and structured sparsity technologies, complex risk control models can achieve several times the throughput of traditional architectures, with inference latency compressed to milliseconds. Every transaction can complete risk assessment in an extremely short time without blocking business processes, making real-time intelligent decision-making truly possible.
Elastic computing power to handle tidal traffic peaks
Computing resources are fully pooled and support dynamic scaling. At traffic peaks, thousands of GPU cores can be elastically scaled out within minutes; after the peak, resources are automatically released. This significantly reduces total cost of ownership and avoids the waste of reserving redundant hardware year-round for occasional peaks.
Financial-grade security and trustworthiness to safeguard compliance
Deeply integrate hardware-level security features, support trusted execution environments to protect privacy-preserving computation data, and provide hardware acceleration for Chinese national cryptographic algorithms. Full-stack software and hardware collaboration ensures data remains usable but not visible throughout its lifecycle, meeting financial regulatory compliance and domestic IT innovation requirements, and building a solid security foundation for an open, shared financial ecosystem.
SOLUTION ARCHITECTURE
Solution architecture
This staged architecture visualizes the current planning path and is not a delivery commitment.- 01
Build a heterogeneous AI computing foundation
Deploy AI server clusters equipped with the latest GPUs/NPUs and build an elastic computing resource pool over a high-speed, low-latency network. Logically isolate training and inference clusters so that offline high-throughput training and online low-latency inference do not interfere with each other, providing powerful and stable computing power for AI applications across all financial scenarios.
- 02
Build an integrated data and AI middle platform
Build a unified big data platform that ingests real-time transaction streams and user behavior logs, and use a stream processing engine for millisecond-level feature engineering. In parallel, build an AI middle platform with an interactive development environment, automated model training pipelines, and a model repository, supporting the full process from feature engineering to one-click model deployment and canary release—significantly shortening AI model iteration cycles.
- 03
Implement low-latency optimization for core transaction paths
For ultra-fast trading scenarios, use low-latency AI servers with all-flash storage and high-precision time synchronization. Reduce OS jitter through kernel bypass and network stack tuning. Combine hardware redundancy, automatic fault isolation, and geo-redundant active-active architecture to ensure microsecond-level response and financial-grade stability even under traffic surges.
- 04
Deploy typical AI applications at scale
Leverage high-performance inference server resources to deploy intelligent risk control, biometric identification, intelligent customer service, RPA, and other applications in batch. Deploy lightweight models at the edge for real-time anti-fraud judgment on terminals, while running large models at the center for complex contract review and investment research analysis—forming an edge-center collaborative AI application matrix that fully unleashes intelligent efficiency.
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