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Duration 14 hours
Course Outline
AI Sovereignty and Local LLM Deployment
- Risks associated with cloud LLMs: data retention, training on user inputs, and foreign jurisdiction.
- Ollama architecture: model server, registry, and OpenAI-compatible API.
- Comparative analysis 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 support for CUDA and ROCm.
- CPU-only fallback options and AVX/AVX2 optimization.
- Docker deployment strategies and persistent volume mapping.
- Multi-GPU setup and VRAM allocation strategies.
Model Management
- Pulling models from the Ollama registry using the ollama pull llama3 command.
- Importing GGUF models from HuggingFace and TheBloke.
- Tradeoffs regarding quantization levels: Q4_K_M, Q5_K_M, and Q8_0.
- Model switching mechanisms and limits on concurrent model loading.
Custom Modelfiles
- Writing Modelfile syntax: FROM, PARAMETER, SYSTEM, and TEMPLATE.
- Tuning parameters such as temperature, top_p, and repeat_penalty.
- System prompt engineering to define role-specific behavior.
- Creating and publishing custom models to a local registry.
API Integration
- Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
- Handling streaming responses and JSON mode.
- Integrating with LangChain, LlamaIndex, and custom applications.
- Implementing authentication and rate limiting via a reverse proxy.
Performance Optimization
- Managing context window sizing and KV cache.
- 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 of prompts and completions.
- Model provenance tracking and hash verification.
Requirements
- Intermediate proficiency in Linux and container administration.
- A high-level understanding of machine learning and transformer models.
- Working knowledge of REST APIs and JSON.
Audience
- AI engineers and developers seeking to replace cloud LLM APIs.
- Organizations with data sensitivity constraints that preclude cloud model usage.
- Government and defense teams requiring air-gapped language model solutions.
