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Duration 14 hours
Course Outline
AI Sovereignty and Local LLM Deployment
- Associated risks of cloud LLMs, including data retention, training on user inputs, and jurisdictional concerns.
- Ollama architecture components: model server, registry, and OpenAI-compatible API.
- Comparative analysis with vLLM, llama.cpp, and Text Generation Inference.
- Licensing terms for models such as 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 techniques.
- Deployment via Docker and management of persistent volume mappings.
- Configuring multi-GPU setups and strategies for VRAM allocation.
Model Management
- Retrieving models from the Ollama registry (e.g., ollama pull llama3).
- Importing GGUF models from HuggingFace and TheBloke.
- Evaluating quantization levels such as Q4_K_M, Q5_K_M, and Q8_0, along with their respective tradeoffs.
- Managing model switching and concurrent loading limits.
Custom Modelfiles
- Syntax for Modelfiles: FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning parameters like temperature, top_p, and repeat_penalty.
- Engineering system prompts to achieve role-specific behaviors.
- Creating and publishing custom models to a local registry.
API Integration
- Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
- Handling streaming responses and enforcing JSON mode.
- Integration with frameworks like LangChain, LlamaIndex, and custom applications.
- Implementing authentication and rate limiting using reverse proxies.
Performance Optimization
- Managing context window sizing and KV cache efficiency.
- Optimizing batch inference and parallel request processing.
- Allocating CPU threads and implementing 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 generated completions.
- Verifying model provenance and hash integrity.
Requirements
- Intermediate proficiency in Linux and container administration.
- A high-level understanding of machine learning and transformer architectures.
- Familiarity with REST APIs and JSON data structures.
Target Audience
- AI engineers and developers looking to migrate away from cloud LLM APIs.
- Organizations with strict data sensitivity policies that restrict cloud model usage.
- Government and defense sectors requiring air-gapped language model solutions.
