GPU vs CPU inference tradeoffs in MLOps - Visual Side-by-Side Comparison

Process Flow - GPU vs CPU inference tradeoffs

Start Inference Request

↓

Check Model Size & Complexity

↓

Choose Hardware

↓

CPU

↓

Run Inference

↓

Measure Latency, Throughput, Cost

↓

Compare Tradeoffs

↓

Select Best Option

↓

End

The flow shows how an inference request is processed by choosing CPU or GPU based on model needs, running inference, measuring performance, and selecting the best option.

Execution Sample

MLOps

Model size = 2GB
Input batch = 32
if model size > 1GB and batch > 16:
  Use GPU
else:
  Use CPU

This simple decision chooses GPU for large models and batches, otherwise CPU.

Process Table

Step	Model Size (GB)	Batch Size	Condition	Hardware Chosen	Latency (ms)	Throughput (samples/sec)	Cost per Inference
1	0.5	8	0.5 > 1 and 8 > 16? False	CPU	50	20	$0.001
2	2	8	2 > 1 and 8 > 16? False	CPU	60	18	$0.001
3	2	32	2 > 1 and 32 > 16? True	GPU	20	100	$0.005
4	0.8	32	0.8 > 1 and 32 > 16? False	CPU	55	22	$0.001
5	3	64	3 > 1 and 64 > 16? True	GPU	18	110	$0.005

💡 Inference hardware chosen based on model size and batch size conditions.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5
Model Size (GB)	N/A	0.5	2	2	0.8	3
Batch Size	N/A	8	8	32	32	64
Hardware Chosen	N/A	CPU	CPU	GPU	CPU	GPU
Latency (ms)	N/A	50	60	20	55	18
Throughput (samples/sec)	N/A	20	18	100	22	110
Cost per Inference	N/A	$0.001	$0.001	$0.005	$0.001	$0.005

Key Moments - 3 Insights

Why does the system choose CPU for a large batch size if the model size is small?

Because the condition requires both model size > 1GB and batch size > 16 to use GPU. If model size is small, even a large batch uses CPU (see Step 4 in execution_table).

Why is GPU latency lower but cost higher compared to CPU?

GPU processes many samples in parallel, reducing latency and increasing throughput, but it uses more power and resources, increasing cost (compare latency and cost columns in execution_table).

What happens if batch size is small but model size is large?

GPU is not chosen because batch size condition fails; CPU is used instead (see Step 2 in execution_table).

Visual Quiz - 3 Questions

Look at the execution table, what hardware is chosen at Step 3?

A CPU

B Neither

C GPU

D Both

💡 Hint

Check the 'Hardware Chosen' column at Step 3 in the execution_table.

At which step does the condition 'model size > 1GB and batch > 16' become false?

A Step 4

B Step 3

C Step 5

D Step 1

💡 Hint

Look at the 'Condition' column in execution_table and find where it is false despite batch > 16.

If batch size increased to 40 at Step 2, what would be the hardware choice?

A CPU

B GPU

C Cannot tell

D Both

💡 Hint

Refer to the condition logic in execution_sample and see if model size and batch size satisfy GPU usage.

Concept Snapshot

GPU vs CPU Inference Tradeoffs:
- Use GPU for large models (>1GB) and large batches (>16) for faster inference.
- CPU is better for small models or small batches due to lower cost.
- GPU offers lower latency and higher throughput but at higher cost.
- Decision depends on model size, batch size, latency needs, and cost constraints.

Full Transcript

This visual execution shows how inference hardware is chosen based on model size and batch size. The decision rule uses both parameters to pick GPU or CPU. GPU is preferred for large models and batches because it processes data faster with lower latency and higher throughput but costs more. CPU is chosen for smaller models or batches to save cost despite slower speed. The execution table traces five example steps with different model sizes and batch sizes, showing hardware choice, latency, throughput, and cost. Variable tracker shows how values change step by step. Key moments clarify common confusions about conditions and tradeoffs. The quiz tests understanding of hardware choice and condition evaluation. This helps beginners see the practical tradeoffs in ML inference hardware selection.

Practice

1. Which of the following is a main advantage of using a GPU over a CPU for machine learning inference?

easy

A. Lower power consumption for small tasks

B. Cheaper hardware cost

C. Better performance on single-threaded tasks

D. Faster processing for large batches of data

Solution

1. Step 1: Understand GPU design for parallelism

   GPUs have many cores designed to handle many operations at once, making them faster for large data batches.

2. Step 2: Compare CPU and GPU strengths

   CPUs are better for single-threaded or small tasks, but GPUs excel at parallel processing, speeding up large inference jobs.

3. Final Answer:

   Faster processing for large batches of data -> Option D

4. Quick Check:

   GPU parallelism = Faster large batch inference [OK]

Hint: GPUs excel at many tasks at once, CPUs at few tasks fast [OK]

Common Mistakes:

• Thinking GPUs always use less power
• Assuming CPUs are cheaper for large-scale inference
• Confusing single-threaded speed with parallel speed

2. Which command correctly runs a TensorFlow model inference on CPU only, ignoring GPUs?

easy

A. CUDA_VISIBLE_DEVICES=0 python inference.py

B. CUDA_VISIBLE_DEVICES='' python inference.py

C. CUDA_VISIBLE_DEVICES=-1 python inference.py

D. CUDA_VISIBLE_DEVICES=all python inference.py

Solution

1. Step 1: Understand CUDA_VISIBLE_DEVICES usage

   Setting CUDA_VISIBLE_DEVICES to an empty string disables GPU visibility, forcing CPU usage.

2. Step 2: Check each option's effect

   CUDA_VISIBLE_DEVICES='' python inference.py disables GPUs correctly; others either select GPUs or use invalid values.

3. Final Answer:

   CUDA_VISIBLE_DEVICES='' python inference.py -> Option B

4. Quick Check:

   Empty CUDA_VISIBLE_DEVICES disables GPU [OK]

Hint: Empty CUDA_VISIBLE_DEVICES means no GPU used [OK]

Common Mistakes:

• Using 0 disables only GPU 0, not all GPUs
• Using -1 is invalid for CUDA_VISIBLE_DEVICES
• Assuming 'all' enables all GPUs but not disables

3. Given this Python snippet for inference timing:

import time
start = time.time()
# Run model inference here
end = time.time()
print(round(end - start, 2))

If GPU inference takes 0.05 seconds and CPU inference takes 0.5 seconds, what will be printed when running on CPU?

medium

A. 0.05

B. 50.0

C. 0.5

D. 5.0

Solution

1. Step 1: Understand timing code output

   The code prints the elapsed time rounded to 2 decimals, so it shows seconds taken.

2. Step 2: Match CPU inference time to output

   CPU inference takes 0.5 seconds, so the printed output is 0.5.

3. Final Answer:

   0.5 -> Option C

4. Quick Check:

   CPU time = 0.5 seconds printed [OK]

Hint: Printed time matches actual elapsed seconds rounded [OK]

Common Mistakes:

• Confusing milliseconds with seconds
• Choosing GPU time instead of CPU time
• Misreading rounding precision

4. You run inference on a GPU but notice it is slower than CPU. Which fix is most likely to improve GPU inference speed?

medium

A. Increase batch size to better use GPU parallelism

B. Reduce batch size to avoid GPU overload

C. Disable GPU and force CPU usage

D. Use single-threaded CPU mode

Solution

1. Step 1: Identify GPU performance factors

   GPUs perform best with larger batch sizes to utilize many cores efficiently.

2. Step 2: Evaluate options for improving GPU speed

   Increasing batch size improves GPU throughput; reducing batch size or disabling GPU lowers performance.

3. Final Answer:

   Increase batch size to better use GPU parallelism -> Option A

4. Quick Check:

   GPU speed improves with larger batches [OK]

Hint: Bigger batches = better GPU use [OK]

Common Mistakes:

• Thinking smaller batches speed up GPU
• Disabling GPU to fix GPU slowness
• Using single-thread CPU instead of GPU

5. You have a small model and low input volume but a tight budget. Which inference setup is best to minimize cost while maintaining reasonable speed?

hard

A. Use CPU inference with small batch sizes

B. Use GPU inference with large batch sizes

C. Use GPU inference with small batch sizes

D. Use CPU inference with large batch sizes

Solution

1. Step 1: Analyze model size and input volume impact

   Small models and low input do not benefit much from GPU parallelism, so GPU cost is less justified.

2. Step 2: Consider budget and batch size tradeoffs

   CPU inference with small batches reduces cost and matches low volume needs without GPU overhead.

3. Final Answer:

   Use CPU inference with small batch sizes -> Option A

4. Quick Check:

   Small model + low volume + budget = CPU small batch [OK]

Hint: Small model + low volume = CPU for cost savings [OK]

Common Mistakes:

• Choosing GPU despite low volume and budget
• Using large batches on CPU causing delays
• Ignoring cost when selecting GPU