Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
AI Sovereignty and Local Deployment of LLMs
- Risks associated with cloud LLMs: data retention risks, input-based training by providers, and foreign jurisdiction issues.
- Understanding 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 CUDA and ROCm support.
- CPU-only fallback options and AVX/AVX2 optimization techniques.
- Docker deployment strategies and persistent volume mapping.
- Multi-GPU setup configurations 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 repositories.
- Evaluating quantization levels: understanding tradeoffs between Q4_K_M, Q5_K_M, and Q8_0.
- Managing model switching and limits on concurrent model loading.
Custom Modelfiles
- Mastering Modelfile syntax: utilizing FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning temperature, top_p, and repeat_penalty parameters.
- Engineering system prompts to drive 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 mode.
- Integrating with LangChain, LlamaIndex, and custom applications.
- Managing authentication and rate limiting via reverse proxy.
Performance Optimization
- Sizing context windows and managing KV cache.
- 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.
- Setting up input filtering and output moderation pipelines.
- Audit logging of prompts and generated completions.
- Verifying model provenance and hashes.
Requirements
- Intermediate proficiency in Linux and container administration.
- High-level understanding of machine learning concepts and transformer models.
- Familiarity with REST APIs and JSON data formats.
Audience
- AI engineers and developers looking to replace cloud LLM APIs.
- Organizations with strict data sensitivity policies that prohibit the use of cloud models.
- Government and defense teams requiring air-gapped language model solutions.
14 Hours
