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.