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.

Practice

1. What is the main goal of latency optimization in AI models?

easy

A. To make AI models respond faster for better user experience

B. To increase the size of the AI model

C. To reduce the accuracy of the AI model

D. To add more layers to the AI model

Solution

1. Step 1: Understand latency meaning

   Latency means the time it takes for a model to give an answer after input.

2. Step 2: Connect latency to user experience

   Lower latency means faster responses, which improves how users feel about the AI.

3. Final Answer:

   To make AI models respond faster for better user experience -> Option A

4. Quick Check:

   Latency optimization = faster response [OK]

Hint: Latency means speed of response, optimize to make it faster [OK]

Common Mistakes:

• Confusing latency with model size
• Thinking latency means accuracy
• Assuming more layers reduce latency

2. Which of the following is a correct Python syntax to measure latency using time module?

easy

A. start = time.sleep(); model.predict(x); end = time.sleep(); latency = end / start

B. start = time.time(); model.predict(x); end = time.time(); latency = end - start

C. start = time.clock(); model.predict(x); end = time.clock(); latency = start - end

D. start = time.now(); model.predict(x); end = time.now(); latency = end + start

Solution

1. Step 1: Identify correct time functions

   Python's time.time() returns current time in seconds; subtracting gives elapsed time.

2. Step 2: Check latency calculation

   Latency = end - start measures duration correctly; other options misuse functions or operations.

3. Final Answer:

   start = time.time(); model.predict(x); end = time.time(); latency = end - start -> Option B

4. Quick Check:

   Latency = end - start time [OK]

Hint: Use time.time() and subtract end-start for latency [OK]

Common Mistakes:

• Using time.now() which does not exist
• Subtracting start - end instead of end - start
• Using time.sleep() which pauses code, not measures time

3. Given this code snippet measuring latency, what will be printed?

import time
start = time.time()
for _ in range(1000000):
    pass
end = time.time()
print(round(end - start, 2))

medium

A. A number close to 1.00

B. An error because of wrong syntax

C. A number close to 10.00

D. A number close to 0.00

Solution

1. Step 1: Understand the loop workload

   The loop runs 1,000,000 times doing nothing (pass), which takes very little time due to Python's loop execution speed.

2. Step 2: Estimate time taken

   On a normal computer, this empty loop takes around 0.03-0.1 seconds, so round(end - start, 2) prints a number close to 0.00.

3. Final Answer:

   A number close to 0.00 -> Option D

4. Quick Check:

   1M empty loops ~0.05s [OK]

Hint: 1 million empty loops take ~0.05 seconds [OK]

Common Mistakes:

• Overestimating time for empty loop (e.g., thinking 1 second)
• Thinking it takes 10 seconds
• Assuming syntax error due to indentation

4. You tried pruning your AI model to reduce latency but latency increased. What is the likely cause?

medium

A. Pruning removed important layers causing slower computation

B. Pruning always increases latency by design

C. Pruning was done incorrectly causing overhead in model execution

D. Latency measurement was done before pruning

Solution

1. Step 1: Understand pruning effect

   Pruning removes less important parts to speed up model, so latency should decrease if done right.

2. Step 2: Identify why latency increased

   If latency increased, pruning likely added overhead or was done incorrectly, causing slower execution.

3. Final Answer:

   Pruning was done incorrectly causing overhead in model execution -> Option C

4. Quick Check:

   Incorrect pruning = more overhead = higher latency [OK]

Hint: Incorrect pruning adds overhead, increasing latency [OK]

Common Mistakes:

• Assuming pruning always slows model
• Ignoring measurement timing
• Thinking pruning removes important layers by default

5. You want to reduce latency of a large AI model for mobile devices. Which combined approach is best?

hard

A. Use quantization to reduce precision and prune unimportant weights

B. Increase model layers and use caching on server

C. Only use caching without changing model size

D. Train a bigger model with more data

Solution

1. Step 1: Identify techniques for latency reduction on mobile

   Quantization reduces number size to speed up computation; pruning removes unneeded parts to shrink model.

2. Step 2: Evaluate options

   Increasing layers or bigger models increase latency; caching helps but alone is not enough for mobile constraints.

3. Final Answer:

   Use quantization to reduce precision and prune unimportant weights -> Option A

4. Quick Check:

   Quantization + pruning = best latency reduction [OK]

Hint: Combine quantization and pruning for mobile latency [OK]

Common Mistakes:

• Thinking bigger models reduce latency
• Relying only on caching for mobile speed
• Ignoring model size impact on mobile devices