Scalability
To scale the system to millions of users, we need to implement some optimizations:
Load balancers distribute incoming requests across multiple app servers. This prevents hot spots and improves throughput.
Horizontally scaling outlets us easily add more servers for components like the app layer, ML inference, databases. Automated scaling handles spikes.
Data partitioning allows splitting conversation data by bot type or user groups. This limits data sizes for higher performance.
Model optimization like distillation, quantization, pruning makes ML inference faster. Model lookups become the throughput bottleneck so optimizing latency is key.
Additional scaling approaches include:
CDNs to cache and distribute static UI assets globally
Replicated databases with data sharding and read replicas
Microservice architecture with independent scaling of components
Serverless functions for burst workloads
Caching for high-throughput requests like static assets
Asynchronous task queues to offload work
By applying these scaling best practices, we can smoothly handle millions of users on a ChatGPT clone system.
