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Duration 14 hours
Course Outline
AI Sovereignty and Local LLM Deployment
- Cloud LLM risks: data retention, input training, and foreign jurisdiction issues.
- Ollama architecture: model server, registry, and OpenAI-compatible API.
- Comparison with vLLM, llama.cpp, and Text Generation Inference.
- Model licensing terms for Llama, Mistral, Qwen, and Gemma.
Installation and Hardware Configuration
- Installing Ollama on Linux with CUDA and ROCm support.
- CPU-only fallback strategies and AVX/AVX2 optimizations.
- Docker deployment techniques and persistent volume mapping.
- Multi-GPU setups and VRAM allocation strategies.
Model Management
- Retrieving models from the Ollama registry: e.g., ollama pull llama3.
- Importing GGUF models from HuggingFace and TheBloke.
- Quantization levels: tradeoffs between Q4_K_M, Q5_K_M, and Q8_0.
- Model switching and limits on concurrent model loading.
Custom Modelfiles
- Writing Modelfile syntax: FROM, PARAMETER, SYSTEM, and TEMPLATE.
- Tuning temperature, top_p, and repeat_penalty parameters.
- System prompt engineering for role-specific behaviors.
- Creating and publishing custom models to a local registry.
API Integration
- Using the OpenAI-compatible /v1/chat/completions endpoint.
- Managing streaming responses and JSON mode.
- Integration with LangChain, LlamaIndex, and custom applications.
- Authentication and rate limiting via reverse proxy.
Performance Optimization
- Context window sizing and KV cache management.
- Batch inference and parallel request handling.
- CPU thread allocation and NUMA awareness.
- Monitoring GPU utilization and memory pressure.
Security and Compliance
- Network isolation for model serving endpoints.
- Input filtering and output moderation pipelines.
- Audit logging for prompts and completions.
- Model provenance verification and hashing.
Requirements
- Intermediate proficiency in Linux and container administration.
- High-level understanding of machine learning and transformer models.
- Familiarity with REST APIs and JSON data structures.
Target Audience
- AI engineers and developers looking to replace cloud LLM APIs.
- Organizations with data sensitivity restrictions that preclude cloud model usage.
- Government and defense teams requiring air-gapped language models.
