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Module 06 · Page 7 of 20 · 8 sections
LLM System Design
Designing an application around a large language model: layers, inference options, data stores and the trade-offs that follow.
What is LLM System Design?
- The process of designing end-to-end architecture to build, deploy and operate Large Language Model applications in production.
- It includes data, model, infrastructure, inference, evaluation, security and monitoring.
- The goal is a scalable, reliable, secure and cost-effective LLM application for real-world use cases.
Key Design Goals
- Scalability
- Handle a large number of users.
- Low Latency
- Provide real-time responses.
- High Availability
- Minimize downtime.
- Cost Efficiency
- Optimize infrastructure cost.
- Security & Privacy
- Protect user data.
- Reliability
- Consistent and accurate responses.
- Observability
- Monitor and debug issues.
- Flexibility
- Support different models and use cases.
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High Level Architecture
ClientWeb / mobile
Load Balancer
API GatewayAuth, rate limiting, routing, logging
Application ServerPrompt handling, business logic, context, response parsing
LLM InferenceOpenAI API, self-hosted, cloud, multi-model
Data LayerVector DB, relational DB, cache, files
Components Deep Dive
- Client
- Web or mobile application.
- Load Balancer
- Distribute traffic across servers.
- API Gateway
- Authentication, rate limiting, routing.
- Application Server
- Handle business logic, prompt engineering and context management.
- LLM Inference
- Call an LLM (OpenAI, Anthropic, Llama, etc.) for text generation.
- Data Layer
- Store documents, embeddings, user data and chat history.
- Monitoring
- Track latency, errors and token usage.
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LLM Inference Options
- OpenAI API
- GPT-4, GPT-4o, etc. (managed).
- Anthropic API
- Claude models (managed).
- Self Hosted
- Llama, Mistral, etc. on your own infrastructure.
- Cloud Providers
- AWS Bedrock, Azure OpenAI, Google Vertex AI.
- Multi-Model Setup
- Route to different models based on use case (cost, capability, latency).
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Data Layer Options
- Vector Database
- Pinecone, Qdrant, Weaviate.
- Relational Database
- PostgreSQL, MySQL.
- Cache
- Redis (store recent responses, sessions).
- File Storage
- S3, GCS (documents, logs).
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Example Use Case: Chatbot Application
User Question
API Gateway
Prompt + Context
LLMGPT-4
Vector DatabaseDocs, embeddings
Response to User
Design Considerations
- 1Handle long context (use RAG, chunking).
- 2Manage token limits and cost.
- 3Implement caching to reduce latency.
- 4Ensure data security and access control.
- 5Use async processing for long running tasks.
- 6Monitor usage, cost and performance.
- 7Plan for model updates and fallback strategy.