Energy-Efficient AI with SLMs Training is a practical training program focused on designing and deploying Small Language Models (SLMs) for low-power, cost-effective, and sustainable AI applications. The course explores how SLMs can reduce computational overhead, energy consumption, and environmental impact while delivering reliable AI capabilities. Participants will learn strategies for building green AI solutions suitable for edge devices, enterprise systems, and resource-constrained environments.

• Understand energy-efficient AI and SLM concepts
• Optimize AI models for low-resource environments
• Apply SLMs for efficient AI applications
• Analyze performance, cost, and energy trade-offs
• Implement sustainable AI development practices

• AI and machine learning practitioners
• Sustainability and green technology teams
• Developers and system architects
• IT and infrastructure professionals
• Organizations focused on energy-efficient computing

• Basic understanding of AI or digital systems
• Familiarity with software or data-driven applications
• No advanced machine learning or deep learning experience required

• Understanding energy-efficient AI principles
• Designing AI solutions using Small Language Models
• Evaluating trade-offs between model size, accuracy, and power usage
• Applying SLMs for low-resource environments
• Supporting sustainable and responsible AI deployment

By the end of this training, participants will be able to design and evaluate energy-efficient AI systems using Small Language Models. Learners will gain practical knowledge to build sustainable AI solutions that minimize resource usage while maintaining effectiveness and reliability.

Introduction to Energy-Efficient AI
• Why energy efficiency matters in AI systems
• Environmental and cost impact of large models
• Role of SLMs in sustainable AI

Understanding Small Language Models (SLMs)
• What are SLMs and how they differ from LLMs
• Model size, architecture, and efficiency considerations
• Common SLM use cases

Green AI Design Principles
• Reducing compute and memory requirements
• Optimizing inference workflows
• Designing AI for efficiency-first use cases

Deployment Strategies for Low-Power Environments
• Edge, on-device, and on-premise deployments
• Hardware considerations and constraints
• Balancing performance and energy consumption

Optimizing SLM Performance and Efficiency
• Prompt optimization for reduced computation
• Managing context length and response size
• Monitoring latency and energy usage

Use Cases for Energy-Efficient AI
• Smart devices and IoT applications
• Enterprise automation with reduced compute costs
• Public sector and sustainability-driven AI solutions

Evaluation and Measurement of Energy Impact
• Measuring efficiency and performance
• Comparing SLMs with larger models
• Cost and energy benchmarking approaches

Ethical, Sustainable, and Responsible AI Practices
• Aligning AI systems with sustainability goals
• Responsible resource usage
• Long-term environmental considerations

Hands-on Green AI Design Exercises
• Real-world energy-efficient AI scenarios
• Guided SLM workflow design
• Participant exercises with feedback

• Fundamentals of SLMs and efficient AI
• Model optimization techniques
• Low-power AI deployment strategies
• Performance and energy evaluation
• Sustainable and responsible AI practices

• Participation in hands-on efficiency exercises
• Use case design and optimization assignments
• Scenario-based assessment

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Energy-Efficient AI with SLMs Training, validating their skills in developing sustainable AI solutions.