OpenAI ChatGPT Architecture
How OpenAI built and scaled ChatGPT: model serving, infrastructure, and handling millions of AI conversations.
25 min read•Advanced
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100M+
Users in 2 months
10B+
Tokens processed/day
175B
Model parameters
<2s
Response latency
System Architecture
Frontend Layer
- React-based web interface with streaming responses
- WebSocket connections for real-time chat
- Global CDN for static asset delivery
- Mobile-responsive design with PWA features
API Gateway
- Rate limiting and quota management
- Authentication and authorization
- Request routing and load balancing
- Content filtering and safety checks
Model Serving
- Multi-GPU inference servers (A100s)
- Model sharding across multiple GPUs
- Dynamic batching for throughput optimization
- KV-cache optimization for conversation context
Infrastructure
- Kubernetes clusters on Azure/AWS
- Auto-scaling based on request queue depth
- Multi-region deployment for low latency
- Monitoring and observability stack
Request Flow
1
User Input Processing
User message → Content moderation → Tokenization
2
Context Management
Conversation history retrieval → Context window optimization
3
Model Inference
GPT model processing → Token generation → Safety filtering
4
Response Streaming
Token streaming → Frontend rendering → Conversation persistence
Related Learning
🚀 LLM Serving & APIs
Learn model serving techniques
📊 GenAI Monitoring
Monitor AI systems at scale
🧩 Practice Problems
AI/ML system design challenges