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Course Outline
AI Sovereignty and Local LLM Deployment
- Risks associated with cloud LLMs: data retention policies, training on input data, and foreign jurisdiction issues.
- Ollama architecture: understanding the model server, registry, and OpenAI-compatible API.
- Comparison with alternatives such as 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 options and AVX/AVX2 optimization techniques.
- Docker deployment strategies and persistent volume mapping.
- Multi-GPU setups and VRAM allocation best practices.
Model Management
- Pulling models from the Ollama registry (e.g., ollama pull llama3).
- Importing GGUF models from HuggingFace and TheBloke repositories.
- Evaluating quantization levels: balancing Q4_K_M, Q5_K_M, and Q8_0 tradeoffs.
- Managing model switching and limits on concurrent model loading.
Custom Modelfiles
- Syntax for writing Modelfiles: using FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning temperature, top_p, and repeat_penalty parameters.
- Engineering system prompts to define role-specific behaviors.
- Creating and publishing custom models to the local registry.
API Integration
- Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
- Implementing streaming responses and JSON output modes.
- Integrating with LangChain, LlamaIndex, and custom applications.
- Configuring authentication and rate limiting via reverse proxy.
Performance Optimization
- Sizing context windows and managing KV cache efficiency.
- Handling batch inference and parallel requests.
- Allocating CPU threads and ensuring NUMA awareness.
- Monitoring GPU utilization and memory pressure.
Security and Compliance
- Implementing network isolation for model serving endpoints.
- Establishing input filtering and output moderation pipelines.
- Audit logging of prompts and completions.
- Verifying model provenance and hash integrity.
Requirements
- Intermediate proficiency in Linux and container administration.
- High-level understanding of machine learning concepts and transformer architectures.
- Familiarity with REST APIs and JSON data structures.
Audience
- AI engineers and developers looking to replace cloud LLM APIs.
- Organizations handling sensitive data that prohibits the use of cloud models.
- Government and defense teams requiring air-gapped language model solutions.
14 Hours
