Backend Infrastructure for AI Applications¶
Deploying AI models requires careful consideration of backend infrastructure - the engine that powers your AI application. This guide covers the key aspects of backend deployment and available tools.
Core Considerations¶
Performance Metrics¶
- Latency: The delay between request and response. Critical for real-time applications and user experience.
- Throughput: The number of requests that can be processed per unit time.
- Model Quality: The accuracy and reliability of model outputs for your specific use case.
For detailed information about computational resources and optimization, see our computation guide.
Deployment Solutions¶
Open Source Libraries¶
High-Performance Serving¶
- vLLM: Uses PagedAttention for 24x throughput improvement
- FlexFlow: Optimized for low-latency serving
- Text Generation Inference: Rust/Python server with gRPC support
Model Management¶
- Torch Serve: PyTorch's official serving solution
- Triton Inference Server: NVIDIA's robust inference server
- litellm: Simplified model deployment and management
Local Development¶
- Ollama: Docker-like experience for local LLM deployment
- llama.cpp: Efficient 4-bit quantization for local inference
- llm CLI: Command-line interface for various LLMs
Cloud Platforms¶
Major Providers¶
- Amazon SageMaker: Comprehensive ML deployment platform
- Azure Machine Learning: Enterprise-grade ML service
- Google Cloud AI Platform: Scalable ML infrastructure
Specialized Services¶
- OpenRouter: Unified API for various open and closed-source models
- Lamini: Simplified LLM training and deployment
- Azure-Chat-GPT: Azure-specific GPT deployment
Tutorials and Resources¶
GCP Production Deployment
Step-by-step guide for deploying large models on Google Cloud Platform
Building LLM Web Apps with Ollama
Tutorial for creating web applications with locally-deployed LLMs