Latency optimization in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is latency in machine learning model deployment?

Latency is the time delay between sending a request to a model and receiving its prediction or output.

beginner

Name one common technique to reduce latency in AI models.

Model quantization, which reduces the precision of numbers in the model to speed up computation and reduce memory use.

intermediate

How does batching requests help with latency optimization?

Batching groups multiple requests together so the model processes them at once, improving throughput and reducing average latency per request.

intermediate

Explain the trade-off between model size and latency.

Smaller models usually run faster and have lower latency but might be less accurate. Larger models are more accurate but slower, increasing latency.

beginner

What role does hardware acceleration play in latency optimization?

Using specialized hardware like GPUs or TPUs speeds up model computations, significantly reducing latency compared to general CPUs.

Which method directly reduces the precision of model weights to speed up inference?

A Data augmentation

B Batching

C Quantization

D Pruning

Quantization reduces the precision of numbers in the model, making computations faster and lowering latency.

What is a downside of aggressively reducing model size to lower latency?

A Lower accuracy

B Higher memory use

C Increased accuracy

D Longer training time

Reducing model size too much can cause the model to lose important information, resulting in lower accuracy.

Batching requests helps latency by:

A Processing requests one by one

B Increasing model size

C Reducing hardware speed

D Grouping requests to process together

Batching groups multiple requests so the model can handle them simultaneously, improving efficiency and reducing average latency.

Which hardware is commonly used to accelerate AI model inference?

A GPU

B CPU

C Hard disk drive

D Monitor

GPUs are specialized hardware designed to speed up parallel computations, reducing latency in AI models.

Latency is best described as:

A The accuracy of a model

B The time delay before a model responds

C The size of the training data

D The number of model layers

Latency measures how long it takes for a model to respond after receiving a request.

Describe three techniques to optimize latency in machine learning models and explain how each helps.

Think about reducing computation time, grouping requests, and using faster machines.

Explain the trade-offs between model accuracy and latency when optimizing AI models.

Consider how making a model smaller affects its predictions and speed.