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Course Outline
AI Sovereignty and Local LLM Deployment
- Risks associated with cloud LLMs: data retention policies, input-based training, and foreign jurisdiction issues.
- Ollama architecture overview: model server, registry, and OpenAI-compatible API.
- Comparison with vLLM, llama.cpp, and Text Generation Inference.
- Model licensing specifics for Llama, Mistral, Qwen, and Gemma.
Installation and Hardware Configuration
- Installing Ollama on Linux with CUDA and ROCm support.
- CPU-only fallback mechanisms and AVX/AVX2 optimization techniques.
- Docker deployment strategies and persistent volume mapping.
- Multi-GPU configuration and VRAM allocation strategies.
Model Management
- Retrieving models from the Ollama registry: example command ollama pull llama3.
- Importing GGUF models from HuggingFace and TheBloke repositories.
- Analyzing quantization levels: tradeoffs between Q4_K_M, Q5_K_M, and Q8_0.
- Managing model switching and limits on concurrent model loading.
Custom Modelfiles
- Syntax structure for Modelfiles: FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning parameters such as temperature, top_p, and repeat_penalty.
- Engineering system prompts to drive role-specific model behavior.
- Creating and publishing custom models to the local registry.
API Integration
- Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
- Implementing streaming responses and JSON mode capabilities.
- Integrating with LangChain, LlamaIndex, and custom applications.
- Managing authentication and rate limiting via reverse proxies.
Performance Optimization
- Configuring context window size and managing KV cache efficiency.
- Handling batch inference and parallel request processing.
- Optimizing CPU thread allocation and ensuring NUMA awareness.
- Monitoring GPU utilization metrics and memory pressure levels.
Security and Compliance
- Establishing network isolation for model serving endpoints.
- Implementing input filtering and output moderation pipelines.
- Maintaining audit logs for prompts and generated completions.
- Verifying model provenance through hash checks.
Requirements
- Intermediate proficiency in Linux and container administration.
- High-level understanding of machine learning concepts and transformer models.
- Familiarity with REST APIs and JSON data formats.
Target Audience
- AI engineers and developers looking to migrate away from cloud LLM APIs.
- Organizations handling sensitive data that restricts the use of cloud models.
- Government and defense teams requiring air-gapped language model solutions.
14 Hours
