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.
Ollama: Self-Hosted Large Language Models Replacing OpenAI and Claude APIs Training Course - Booking
Ollama: Self-Hosted Large Language Models Replacing OpenAI and Claude APIs Training Course - Enquiry
Ollama: Self-Hosted Large Language Models Replacing OpenAI and Claude APIs - Consultancy Enquiry
Upcoming Courses
Related Courses
Advanced Ollama Model Debugging & Evaluation
35 HoursThe Advanced Ollama Model Debugging & Evaluation course provides a comprehensive deep dive into diagnosing, testing, and assessing model behavior for local or private Ollama deployments.
This instructor-led live training, available online or onsite, targets advanced AI engineers, ML Ops professionals, and QA specialists who aim to ensure the reliability, fidelity, and operational readiness of Ollama-based models in production environments.
Upon completion of this training, participants will be able to:
- Conduct systematic debugging of Ollama-hosted models and reliably reproduce failure scenarios.
- Design and implement robust evaluation pipelines using both quantitative and qualitative metrics.
- Establish observability measures (logs, traces, and metrics) to monitor model health and detect drift.
- Automate testing, validation, and regression checks, integrating them seamlessly into CI/CD pipelines.
Course Format
- Interactive lectures and discussions.
- Hands-on labs and debugging exercises utilizing Ollama deployments.
- Case studies, collaborative troubleshooting sessions, and automation workshops.
Course Customization Options
- To request a tailored training version of this course, please contact us to arrange.
Building Private AI Workflows with Ollama
14 HoursThis instructor-led, live training in Romania (online or onsite) is designed for advanced professionals seeking to implement secure and efficient AI-driven workflows using Ollama.
Upon completion of this training, participants will be able to:
- Deploy and configure Ollama for private AI processing.
- Integrate AI models into secure enterprise workflows.
- Optimize AI performance while maintaining strict data privacy.
- Automate business processes using on-premise AI capabilities.
- Ensure compliance with enterprise security and governance policies.
Deploying and Optimizing LLMs with Ollama
14 HoursThis instructor-led, live training in Romania (online or onsite) is designed for intermediate-level professionals aiming to deploy, optimize, and integrate LLMs using Ollama.
Upon completion of this training, participants will be equipped to:
- Install and deploy LLMs using Ollama.
- Tune AI models for enhanced performance and operational efficiency.
- Harness GPU acceleration to boost inference speeds.
- Seamlessly integrate Ollama into existing workflows and applications.
- Monitor and sustain AI model performance over time.
EXO: End-to-End Local AI Cluster Deployment
21 HoursThis instructor-led, live training in Romania (online or onsite) is aimed at system administrators and DevOps engineers who wish to deploy, configure, and manage EXO clusters for private LLM inference across multiple Apple Silicon or Linux nodes.
EXO for DevOps: Building Private AI Infrastructure
21 HoursThis instructor-led, live training in Romania (online or onsite) is aimed at DevOps engineers and infrastructure architects who wish to automate the provisioning, monitoring, and lifecycle management of private AI clusters built on EXO.
EXO Security and Governance: Offline Model Management
14 HoursThis instructor-led, live training in Romania (online or onsite) is aimed at security engineers and compliance officers who wish to harden EXO deployments, control model access, and govern AI workloads running entirely on-premise.
Fine-Tuning and Customizing AI Models on Ollama
14 HoursThis instructor-led, live training in Romania (online or onsite) is designed for advanced professionals who want to fine-tune and customize AI models on Ollama to achieve better performance and support domain-specific applications.
By the conclusion of this training, participants will be able to:
- Set up an efficient environment for fine-tuning AI models on Ollama.
- Prepare datasets for supervised fine-tuning and reinforcement learning.
- Optimize AI models for performance, accuracy, and efficiency.
- Deploy customized models in production environments.
- Evaluate model improvements and ensure robustness.
Secure Local Agentic AI: On-Prem Ollama Development for Regulated Industries
21 HoursThis instructor-led, live training in Romania (online or onsite) is designed for developers and technical teams who want to use Ollama and open models to create and operate private agentic AI solutions on internal infrastructure.
By the end of this training, participants will be able to: install and configure Ollama, evaluate and run open models locally, develop simple agentic and retrieval-based workflows, and implement security and governance controls for regulated environments.
Multimodal Applications with Ollama
21 HoursOllama is a platform designed for running and fine-tuning large language and multimodal models on local devices.
This instructor-led live training, available either online or onsite, targets advanced-level ML engineers, AI researchers, and product developers seeking to build and deploy multimodal applications using Ollama.
Upon completing this training, participants will be able to:
- Configure and execute multimodal models with Ollama.
- Integrate text, image, and audio inputs for practical applications.
- Construct systems for document understanding and visual question answering.
- Develop multimodal agents capable of reasoning across different data modalities.
Course Format
- Interactive lectures and discussions.
- Practical exercises using real-world multimodal datasets.
- Live-lab implementation of multimodal pipelines via Ollama.
Course Customization Options
- To request a customized version of this course, please contact us to arrange details.
Getting Started with Ollama: Running Local AI Models
7 HoursThis instructor-led, live training in Romania (online or onsite) is designed for beginner-level professionals who want to install, configure, and utilize Ollama to run AI models on their local machines.
By the conclusion of this training, participants will be able to:
- Grasp the fundamentals of Ollama and its capabilities.
- Set up Ollama to run local AI models.
- Deploy and interact with LLMs using Ollama.
- Optimize performance and resource usage for AI workloads.
- Explore use cases for local AI deployment across various industries.
Ollama & Data Privacy: Secure Deployment Patterns
14 HoursOllama is a platform designed for running large language and multimodal models locally, while also supporting secure deployment strategies.
This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who want to deploy Ollama with robust data privacy and regulatory compliance measures.
By the end of this training, participants will be able to:
- Deploy Ollama securely in containerized and on-premises environments.
- Apply differential privacy techniques to protect sensitive data.
- Implement secure logging, monitoring, and auditing practices.
- Enforce data access control in alignment with compliance requirements.
Course Format
- Interactive lectures and discussions.
- Hands-on labs focused on secure deployment patterns.
- Case studies and practical exercises centered on compliance.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
Ollama Applications in Finance
14 HoursOllama serves as a lightweight platform designed for running large language models locally.
This instructor-led live training, available either online or on-site, is tailored for finance practitioners and IT professionals at an intermediate level who aim to implement, customize, and operationalize AI solutions based on Ollama within financial contexts.
Upon completing this training, participants will acquire the skills necessary to:
- Deploy and configure Ollama to ensure secure operations within financial environments.
- Incorporate local large language models into analytical and reporting processes.
- Adapt models to align with finance-specific terminology and tasks.
- Apply best practices regarding security, privacy, and compliance.
Course Format
- Interactive lectures and discussions.
- Practical exercises using financial data.
- Live-lab implementation of scenarios focused on finance.
Options for Course Customization
- To request a tailored training program for this course, please reach out to us to make arrangements.
Ollama Applications in Healthcare
14 HoursOllama is a lightweight platform designed for running large language models locally.
This instructor-led live training, available both online and onsite, is tailored for intermediate-level healthcare professionals and IT teams looking to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative settings.
After completing this training, participants will be able to:
- Install and configure Ollama for secure use in healthcare environments.
- Integrate local large language models into clinical workflows and administrative processes.
- Customize models for healthcare-specific terminology and tasks.
- Apply best practices for privacy, security, and regulatory compliance.
Course Format
- Interactive lectures and discussions.
- Hands-on demonstrations and guided exercises.
- Practical implementation in a sandboxed healthcare simulation environment.
Customization Options
- To request a customized training session for this course, please contact us to arrange.
Ollama for Responsible AI and Governance
14 HoursOllama serves as a platform for executing large language and multimodal models locally, with built-in support for governance and responsible AI practices.
This instructor-led live training, available both online and onsite, targets intermediate to advanced professionals looking to integrate fairness, transparency, and accountability into applications powered by Ollama.
Upon completing this course, participants will be able to:
- Apply responsible AI principles during Ollama deployments.
- Implement strategies for content filtering and bias mitigation.
- Design governance workflows that ensure AI alignment and auditability.
- Establish monitoring and reporting frameworks to support compliance.
Course Format
- Interactive lectures and discussions.
- Hands-on labs for designing governance workflows.
- Case studies and compliance-focused exercises.
Customization Options
- To request a customized version of this training, please contact us to make arrangements.
Sovereign AI for Regulated Organizations: Controlling Data, Models and Inference Environments
7 HoursThis instructor-led training in Romania (delivered online or onsite) targets mid-level IT executives, compliance officers, security specialists, and enterprise architects seeking to utilize sovereign AI principles and governance strategies. The goal is to design AI environments that protect sensitive data, adhere to localization rules, and decrease vendor lock-in.
By the end of this session, participants will be able to explain sovereign AI concepts, assess hosting and governance alternatives, define controls for prompts and logs, and develop a practical roadmap for adoption.
