Architecting Scalable AI Systems

## The Challenge of Scale in AI
Building AI systems that can handle millions of requests while maintaining low latency is one of the most significant engineering challenges today. It's not just about the model; it's about the infrastructure surrounding it.

### Key Architectural Patterns
1. **Distributed Inference**: Spreading the computational load across multiple GPU nodes to ensure high availability.
2. **Asynchronous Processing**: Utilizing message queues like RabbitMQ or Kafka to decouple user requests from heavy model computations.
3. **Edge Computing**: Moving lightweight models closer to the user to reduce network latency.

### Conclusion
Scalability in AI requires a holistic approach that balances model performance with robust distributed systems engineering.