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Future of AI Inference

This course details the critical techniques for optimising AI model inference performance. Learners will understand how to apply quantization, model pruning, and hardware-specific acceleration to significantly reduce latency and computational cost for deploying large language models and other AI systems. After completing this course, you will be able to diagnose inference bottlenecks and implement effective strategies using tools like ONNX Runtime and NVIDIA TensorRT.

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Lessons
8
Price
Free
Course Curriculum

What you'll learn

4 Modules
8 Lessons
~40m total
01
01
Module 01
Techniques for Inference Optimisation
This module introduces core techniques for optimising AI model inference, focusing on methods to reduce computational load and memory footprint. We will cover quantization and model pruning, explaining their theoretical basis and practical application for models like large language models.
2 lessons10 min
0%
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02
02
Module 02
Hardware Acceleration and Deployment
This module focuses on leveraging specialised hardware and runtime environments for accelerated AI inference. We will explore how to utilise GPUs and TPUs, and integrate optimisation tools like ONNX Runtime and NVIDIA TensorRT to achieve significant performance gains in real-world deployments.
2 lessons10 min🔒 Locked
0%
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03
03
Module 03
Fundamentals of Inference Optimisation
This module introduces the core concepts and challenges of AI inference, focusing on why optimisation is critical for deploying large models like Llama 3 efficiently. It covers the trade-offs between model accuracy and computational resources.
2 lessons10 min🔒 Locked
0%
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04
04
Module 04
Core Optimisation Techniques
This module delves into practical techniques for AI model optimisation, including quantization, pruning, and knowledge distillation. Learners will understand how to apply these methods to improve inference efficiency for models like Llama 3.
2 lessons10 min🔒 Locked
0%
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Future of AI Inference
29/06/2026
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