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 HoursAdvanced Debugging and Evaluation of Ollama Models is a comprehensive course dedicated to diagnosing, testing, and assessing model performance within local or private Ollama environments.
Delivered as instructor-led live training (available online or onsite), this program targets advanced AI engineers, ML Ops specialists, and QA experts seeking to ensure the reliability, accuracy, and operational stability of production-grade Ollama models.
Upon completion, participants will be equipped to:
- Systematically debug Ollama-hosted models and reliably replicate failure scenarios.
- Design and execute robust evaluation pipelines using both quantitative and qualitative metrics.
- Implement observability frameworks (logs, traces, metrics) to monitor model health and detect drift.
- Automate testing, validation, and regression checks within CI/CD pipelines.
Course Format
- Interactive lectures and discussions.
- Hands-on labs and debugging exercises utilizing Ollama deployments.
- Case studies, group troubleshooting sessions, and automation workshops.
Customization Options
- To request tailored training for this course, please contact us to arrange.
Building Private AI Workflows with Ollama
14 HoursThis live, instructor-led training in Turkey (online or on-site) is tailored for advanced-level professionals who aim to implement secure and efficient AI-driven workflows using Ollama.
By the end 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 data privacy.
- Automate business processes with 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 Turkey (online or onsite) is designed for intermediate-level professionals who wish to deploy, optimize, and integrate LLMs using Ollama.
By the end of this training, participants will be able to:
- Set up and deploy LLMs using Ollama.
- Optimize AI models for performance and efficiency.
- Leverage GPU acceleration for improved inference speeds.
- Integrate Ollama into workflows and applications.
- Monitor and maintain AI model performance over time.
EXO: End-to-End Local AI Cluster Deployment
21 HoursThis instructor-led, live training in Turkey (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 Turkey (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 Turkey (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 Turkey (online or onsite) is designed for advanced-level professionals seeking to fine-tune and customize AI models on Ollama to enhance performance and support domain-specific applications.
Upon completion of this training, participants will be able to:
- Establish an efficient environment for fine-tuning AI models on Ollama.
- Prepare datasets for supervised fine-tuning and reinforcement learning.
- Optimize AI models for improved performance, accuracy, and efficiency.
- Deploy customized models within production environments.
- Assess model improvements and ensure system robustness.
Secure Local Agentic AI: On-Prem Ollama Development for Regulated Industries
21 HoursThis instructor-led, live training in Turkey (online or onsite) is designed for developers and technical teams who wish to utilize Ollama and open models to build 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, create simple agentic and retrieval-based workflows, and apply security and governance controls for regulated environments.
Multimodal Applications with Ollama
21 HoursOllama serves as a platform that facilitates the execution and fine-tuning of large language and multimodal models on local machines.
This instructor-led, live training session (available online or on-site) is designed for advanced ML engineers, AI researchers, and product developers aiming to create and deploy multimodal applications using Ollama.
Upon completion of this training, participants will be equipped to:
- Configure and execute multimodal models via Ollama.
- Combine text, image, and audio inputs for practical applications.
- Create document comprehension and visual question-answering systems.
- Develop multimodal agents capable of cross-modal reasoning.
Course Format
- Interactive lectures and discussions.
- Practical exercises using real multimodal datasets.
- Live laboratory implementation of multimodal pipelines with Ollama.
Customization Options
- For customized training requests, please get in touch with us.
Getting Started with Ollama: Running Local AI Models
7 HoursThis instructor-led, live training in Turkey (online or onsite) targets beginner-level professionals who wish to install, configure, and utilize Ollama for running AI models on their local machines.
Upon completing this training, participants will be able to:
- Grasp the core principles and capabilities of Ollama.
- Configure Ollama for local AI model execution.
- Deploy and interact with LLMs using Ollama.
- Enhance performance and resource efficiency for AI workloads.
- Examine real-world use cases for local AI deployment across various industries.
Ollama & Data Privacy: Secure Deployment Patterns
14 HoursOllama is a platform that enables the local execution of large language and multimodal models while facilitating secure deployment strategies.
This instructor-led live training, available either online or onsite, is designed for intermediate-level professionals looking to deploy Ollama with robust data privacy and regulatory compliance measures.
Upon completing this training, participants will be capable of:
- Securely deploying Ollama in both containerized and on-premises environments.
- Applying differential privacy techniques to protect sensitive data.
- Implementing secure logging, monitoring, and auditing practices.
- Enforcing data access controls that align with compliance requirements.
Course Format
- Interactive lectures and discussions.
- Hands-on labs focusing on secure deployment patterns.
- Case studies and practical exercises centered on compliance.
Course Customization Options
- To arrange customized training for this course, please contact us.
Ollama Applications in Finance
14 HoursOllama serves as a streamlined platform designed for executing large language models directly on local infrastructure.
This instructor-led live training, available both online and onsite, is tailored for finance professionals and IT staff at an intermediate level who aim to implement, tailor, and deploy AI solutions powered by Ollama within financial contexts.
Upon successful completion of this program, attendees will acquire the competencies required to:
- Deploy and configure Ollama to ensure secure operations in financial settings.
- Embed local large language models into data analysis and reporting processes.
- Adapt models to align with financial-specific terminology and objectives.
- Implement best practices for security, privacy, and regulatory compliance.
Course Format
- Engaging lectures paired with interactive discussions.
- Practical exercises involving financial datasets.
- Live laboratory sessions focused on finance-specific scenarios.
Customization Options
- For inquiries regarding customized training modules for this course, please get in touch with us to arrange accordingly.
Ollama Applications in Healthcare
14 HoursOllama is a lightweight platform designed for running large language models locally.
This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare professionals and IT teams seeking to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative settings.
Upon completion of this training, participants will be able to:
- Install and configure Ollama to ensure secure usage in healthcare environments.
- Integrate local Large Language Models (LLMs) into clinical workflows and administrative processes.
- Customize models to handle 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 within a sandboxed healthcare simulation environment.
Customization Options
- To request customized training 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, thereby supporting governance and responsible AI practices.
This instructor-led, live training, available either online or onsite, targets intermediate to advanced professionals aiming to embed fairness, transparency, and accountability within Ollama-driven applications.
Upon completion of this training, participants will be capable of:
- Applying responsible AI principles during Ollama deployments.
- Implementing strategies for content filtering and bias mitigation.
- Designing governance workflows to ensure AI alignment and auditability.
- Establishing monitoring and reporting frameworks to meet compliance requirements.
Course Format
- Interactive lectures and discussions.
- Hands-on labs focused on designing governance workflows.
- Case studies and exercises centered on compliance.
Course Customization Options
- For customized training tailored to your needs, please contact us to make arrangements.
Sovereign AI for Regulated Organizations: Controlling Data, Models and Inference Environments
7 HoursThis instructor-led, live training in Turkey (online or onsite) is designed for intermediate-level IT leaders, compliance professionals, security teams, and enterprise architects seeking to leverage sovereign AI principles and governance practices. The goal is to design AI environments that protect sensitive data, fulfill localization requirements, and reduce vendor lock-in.
By the end of this training, participants will be able to explain sovereign AI concepts, evaluate hosting and governance alternatives, define controls for prompts and logs, and develop a practical adoption roadmap.
