Artificial intelligence and machine learning are often described as some of the most demanding tasks in modern computing. In IB Computer Science, students are expected to understand why GPUs are preferred over CPUs for many AI and machine learning applications — not just that they are “faster”.
The reason lies in how AI algorithms work and how GPUs are designed.
What Machine Learning Workloads Look Like
At a basic level, many machine learning tasks involve:
- Large datasets
- Repeated calculations
- Mathematical operations on arrays or matrices
For example, training a model may require:
- Performing the same calculation millions of times
- Applying identical operations to different data points
- Processing data in parallel
These characteristics strongly influence hardware choice.
Why CPUs Are Not Ideal for Machine Learning
CPUs are designed for:
- Sequential processing
- Complex control flow
- Branching and decision-making
While CPUs are very powerful, they:
- Have relatively few cores
- Are optimised for flexibility rather than throughput
This makes CPUs inefficient for tasks where the same operation must be repeated across large datasets.
Why GPUs Are Well-Suited to AI
GPUs are designed to handle massive parallelism.
Key features that make GPUs ideal for AI include:
- Thousands of small cores
- Ability to perform the same instruction on many data values at once
- High throughput for arithmetic operations
In machine learning, this allows:
- Faster training of models
- Efficient processing of large datasets
- Significant performance improvements compared to CPUs
Operations like matrix multiplication, which are common in AI, map perfectly onto GPU architecture.
Parallel Processing and Machine Learning
Machine learning algorithms often process data in batches.
GPUs excel here because they can:
- Apply the same operation to many data points simultaneously
- Process entire matrices in parallel
- Reduce overall computation time
This parallel approach dramatically speeds up training and inference.
Energy Efficiency and Performance
Another reason GPUs are used is efficiency.
For large-scale machine learning tasks:
- GPUs can perform more calculations per unit of energy
- Training times are reduced
- Systems are more cost-effective at scale
This is especially important in data centres and cloud computing environments.
GPUs in Real-World AI Applications
GPUs are commonly used in:
- Image recognition
- Natural language processing
- Autonomous vehicles
- Recommendation systems
- Scientific simulations
In each case, large volumes of similar calculations are performed repeatedly — exactly what GPUs are designed for.
What IB Students Are Expected to Explain
In IB Computer Science HL, students should be able to:
- Link GPU architecture to parallel processing
- Explain why GPUs outperform CPUs for AI tasks
- Avoid vague statements like “GPUs are faster”
- Use terms like parallelism, throughput, and matrix operations
Clear cause-and-effect explanations are rewarded.
Common Student Mistakes
Students often:
- Treat GPUs as upgraded CPUs
- Ignore the role of parallel processing
- Focus only on speed, not architecture
- Forget why AI algorithms need parallelism
These misunderstandings lead to weak exam answers.
Final Thoughts
GPUs are used in AI and machine learning because they are architecturally suited to large-scale parallel computation. Their ability to process many data points at the same time makes them far more efficient than CPUs for training and running machine learning models.
Understanding this connection between hardware design and software behaviour is exactly what IB Computer Science HL is designed to assess.
