Ollama: Self-Hosted Large Language Models Replacing OpenAI and Claude APIs Training Course
Ollama is an open-source tool for running large language models locally on consumer and enterprise hardware. It abstracts model quantization, GPU allocation, and API serving into a single command-line interface, enabling organizations to self-host LLMs like Llama, Mistral, and Qwen without sending prompts or data to OpenAI, Anthropic, or Google.
This instructor-led, live training (online or onsite) is aimed at intermediate AI engineers and platform operators who wish to use Ollama to replace cloud LLM APIs with self-hosted, sovereign language model inference.
By the end of this training, participants will be able to:
- Install Ollama on Linux, macOS, and Windows with GPU support.
- Pull, quantize, and serve models from the Ollama registry and HuggingFace.
- Build custom Modelfiles with system prompts and parameter tuning.
- Integrate local LLMs with applications via the OpenAI-compatible API.
- Optimize inference performance for CPU-only and multi-GPU setups.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
AI Sovereignty and LLM Local Deployment
- Risks of cloud LLMs: data retention, training on inputs, foreign jurisdiction.
- Ollama architecture: model server, registry, and OpenAI-compatible API.
- Comparison with vLLM, llama.cpp, and Text Generation Inference.
- Model licensing: Llama, Mistral, Qwen, and Gemma terms.
Installation and Hardware Setup
- Installing Ollama on Linux with CUDA and ROCm support.
- CPU-only fallback and AVX/AVX2 optimization.
- Docker deployment and persistent volume mapping.
- Multi-GPU setup and VRAM allocation strategies.
Model Management
- Pulling models from the Ollama registry: ollama pull llama3.
- Importing GGUF models from HuggingFace and TheBloke.
- Quantization levels: Q4_K_M, Q5_K_M, Q8_0 tradeoffs.
- Model switching and concurrent model loading limits.
Custom Modelfiles
- Writing Modelfile syntax: FROM, PARAMETER, SYSTEM, TEMPLATE.
- Temperature, top_p, and repeat_penalty tuning.
- System prompt engineering for role-specific behavior.
- Creating and publishing custom models to local registry.
API Integration
- OpenAI-compatible /v1/chat/completions endpoint.
- Streaming responses and JSON mode.
- Integrating with LangChain, LlamaIndex, and custom apps.
- Authentication and rate limiting with reverse proxy.
Performance Optimization
- Context window sizing and KV cache management.
- Batch inference and parallel request handling.
- CPU thread allocation and NUMA awareness.
- Monitoring GPU utilization and memory pressure.
Security and Compliance
- Network isolation for model serving endpoints.
- Input filtering and output moderation pipelines.
- Audit logging of prompts and completions.
- Model provenance and hash verification.
Requirements
- Intermediate Linux and container administration.
- Understanding of machine learning and transformer models at high level.
- Familiarity with REST APIs and JSON.
Audience
- AI engineers and developers replacing cloud LLM APIs.
- Organizations with data sensitivity preventing cloud model usage.
- Government and defense teams requiring air-gapped language models.
Open Training Courses require 5+ participants.
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