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Back-End 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 Components

Computation and Resources

For detailed information about computational resources, hardware requirements, and optimization strategies, see our computation guide.

Model Operations

For comprehensive coverage of model deployment, monitoring, and management, see our LLM Operations guide.

Pre-trained Models

For information about available models, their characteristics, and selection criteria, see our pre-trained models guide.

Orchestration

For details about frameworks and tools for managing AI workflows, see our orchestration guide.

Data Processing

For information about data handling in backend systems, see our data processing guide.

Deployment Solutions

Open Source Libraries

High-Performance Serving
Model 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
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

Implementation Resources

Tutorials
Additional Resources