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Duration 14 hours
Course Outline
AI Sovereignty and Local LLM Deployment
- Assessing risks associated with cloud LLMs, including data retention, input training, and jurisdictional concerns.
- Exploring Ollama's architecture, covering the model server, registry, and OpenAI-compatible API.
- Evaluating Ollama against alternatives such as vLLM, llama.cpp, and Text Generation Inference.
- Reviewing model licensing terms for Llama, Mistral, Qwen, and Gemma.
Installation and Hardware Configuration
- Setting up Ollama on Linux with CUDA and ROCm compatibility.
- Implementing CPU-only fallbacks and AVX/AVX2 optimizations.
- Deploying via Docker with persistent volume mapping.
- Configuring multi-GPU setups and strategies for VRAM allocation.
Model Management
- Retrieving models from the Ollama registry, such as using 'ollama pull llama3'.
- Importing GGUF models from HuggingFace and TheBloke.
- Analyzing quantization levels, including Q4_K_M, Q5_K_M, and Q8_0 trade-offs.
- Managing model switching and understanding concurrent loading limits.
Custom Modelfiles
- Mastering Modelfile syntax, including FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning parameters like temperature, top_p, and repeat_penalty.
- Crafting system prompts to enforce role-specific behaviors.
- Creating and publishing custom models to local registries.
API Integration
- Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
- Handling streaming responses and JSON mode outputs.
- Integrating with frameworks like LangChain, LlamaIndex, and custom applications.
- Implementing authentication and rate limiting via reverse proxies.
Performance Optimization
- Optimizing context window sizing and KV cache management.
- Improving batch inference and parallel request handling.
- Allocating CPU threads with NUMA awareness.
- Monitoring GPU utilization and memory pressure metrics.
Security and Compliance
- Establishing network isolation for model serving endpoints.
- Implementing input filtering and output moderation pipelines.
- Maintaining audit logs for prompts and completions.
- Ensuring model provenance and hash verification integrity.
Requirements
- Intermediate skills in Linux and container administration.
- A conceptual grasp of machine learning and transformer models.
- Proficiency with REST APIs and JSON structures.
Target Audience
- AI engineers and developers seeking to migrate away from cloud LLM APIs.
- Organizations with strict data sensitivity policies that preclude cloud model usage.
- Government and defense entities requiring air-gapped language model capabilities.
