AI

Serve LLM locally

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Key Components:

  1. User Interface Layer - How users interact with the model (web UI, API, CLI)
  2. Application Layer - Request handling, authentication, and queuing
  3. Model Serving Layer - The inference engines and optimization techniques:
    • llama.cpp/Ollama - Popular C++ implementations for CPU/GPU
    • vLLM - High-performance GPU inference
    • PyTorch/Transformers - Python-based serving
    • GGML/GGUF - Efficient model formats
  4. Hardware Requirements:
    • CPU: High core count for parallel processing
    • GPU: RTX 4090, A100, or similar with substantial VRAM
    • RAM: 32GB+ system memory
    • Storage: Fast NVMe SSD for model loading
  5. Model Storage - Where model files, weights, and cache are stored
  6. Configuration - Settings for model behavior and hardware optimization

Popular Local LLM Stacks:

  • Ollama - Simplest setup, handles everything automatically
  • llama.cpp - Maximum control and efficiency
  • vLLM - Best performance for GPU setups
  • Hugging Face Transformers - Most flexible for custom models