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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 issues.
- Ollama architecture: overview of the model server, registry, and OpenAI-compatible API.
- Comparative analysis with vLLM, llama.cpp, and Text Generation Inference.
- Licensing considerations for Llama, Mistral, Qwen, and Gemma models.
Installation and Hardware Configuration
- Installing Ollama on Linux with CUDA and ROCm support.
- Configuring CPU-only fallbacks and AVX/AVX2 optimizations.
- Deploying via Docker with persistent volume mapping.
- Setting up multi-GPU environments and VRAM allocation strategies.
Model Management
- Retrieving models from the Ollama registry using commands like `ollama pull llama3`.
- Importing GGUF models from HuggingFace and TheBloke.
- Evaluating tradeoffs between quantization levels: Q4_K_M, Q5_K_M, and Q8_0.
- Managing model switching and concurrent model loading limits.
Custom Modelfiles
- Writing Modelfile syntax using FROM, PARAMETER, SYSTEM, and TEMPLATE.
- Tuning parameters such as temperature, top_p, and repeat_penalty.
- Engineering system prompts for specific role-based behaviors.
- Creating and publishing custom models to a local registry.
API Integration
- Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
- Implementing streaming responses and JSON mode.
- Integrating with frameworks like LangChain, LlamaIndex, and custom applications.
- Configuring authentication and rate limiting via a reverse proxy.
Performance Optimization
- Managing context window sizing and KV cache efficiency.
- Optimizing batch inference and parallel request handling.
- Allocating CPU threads with NUMA awareness.
- Monitoring GPU utilization and memory pressure.
Security and Compliance
- Isolating networks for model serving endpoints.
- Implementing input filtering and output moderation pipelines.
- Maintaining audit logs for prompts and completions.
- Ensuring model provenance through hash verification.
Requirements
- Intermediate proficiency in Linux and container administration.
- A high-level understanding of machine learning and transformer models.
- Familiarity with REST APIs and JSON structures.
Audience
- AI engineers and developers looking 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.
