# Scaling Self-Hosted LLMs for Developer Documentation Search ## Project Overview A technology company that builds hardware and software platforms for developers needed to improve their developer documentation accessibility. Their existing documentation was difficult to navigate, causing challenges for developers trying to understand their products. The company wanted a self-hosted LLM solution to maintain control over proprietary information and build internal capabilities. ## Technical Architecture ### Core Components - Orchestration service using FastAPI - Vector database for RAG implementation - Guard rails service for content safety - Model server running Mistral 7B Instruct - AWS GPU instances for inference ### System Flow - Developer queries enter through orchestration service - Vector database retrieves relevant documentation context - Guard rails service performs safety checks and topic validation - LLM generates responses using retrieved context - Custom model trained for topic validation ## Performance Optimization Approach ### Benchmarking Strategy - Developed test scenarios using Locust - Created progressive load testing scenarios: ### Key Metrics - Latency (response time) - Throughput (tokens per second) - Request rate (successful requests per unit time) - Additional tracking: ## Optimization Solutions ### vLLM Implementation - Addressed GPU memory bottleneck - Implemented PageAttention for key-value lookups - Achieved improvements: ### Horizontal Scaling with Ray Serve - Enabled multi-server deployment - Provided autoscaling capabilities - Integrated with cloud provider autoscaling - Implemented GPU sharing across services: ### Integration Benefits - Successful combination of vLLM and Ray Serve - Balanced vertical and horizontal scaling - Optimized GPU utilization across services ## Guard Rails Implementation ### Safety Features - Topic validation using custom trained model - Prevention of: - Proprietary information protection ## Key Learnings and Best Practices ### Performance Optimization - Always benchmark before optimization - Focus on user experience metrics - Consider both vertical and horizontal scaling - Monitor GPU utilization and costs ### System Design - Implement robust guard rails - Use RAG for accurate responses - Balance performance with cost efficiency - Consider GPU sharing across services ### Development Process - Start with proof of concept - Establish baseline metrics - Use progressive load testing - Document all configuration changes ## Future Considerations ### Scalability - Monitor emerging optimization techniques - Evaluate new model deployment strategies - Consider alternative GPU sharing approaches ### Technology Evolution - Stay current with framework updates - Evaluate new model options - Monitor cloud provider capabilities ## Infrastructure Decisions ### Cloud Setup - AWS GPU instances for inference - Consideration of cost vs performance - Autoscaling configuration - GPU resource sharing ### Monitoring and Maintenance - Performance metric tracking - Version control integration - Environment configuration management - Benchmark result documentation This case study demonstrates the importance of systematic performance optimization in LLM deployments, combining both vertical optimization through vLLM and horizontal scaling through Ray Serve. The implementation of comprehensive guard rails and careful attention to benchmarking resulted in a production-ready system that effectively serves developer documentation needs while maintaining security and performance standards.