GPU vs CPU Inference: 5 Scenarios, Real Costs & Latency

The $400/Month Surprise

I ran the same BERT model on a T4 GPU and a 4-core CPU for a month. The GPU was faster, obviously. But it cost $400 more than the CPU setup, which handled 95% of requests under our 200ms SLA just fine.

Most benchmarks compare raw throughput or single-request latency. They skip the part where you actually pick hardware for a production system with a budget, an SLA, and real traffic patterns. This post runs five realistic scenarios — from a personal side project to a high-traffic API — and shows when GPUs pay for themselves and when you're just burning money.

Photo by Andrey Matveev on Pexels

Test Setup: Models, Hardware, Traffic

I tested three model sizes across two compute tiers:

Models:

• BERT-base (110M params): text classification, sequence length 128
• ResNet50 (25M params): image classification, 224×224 input
• Whisper-tiny (39M params): speech-to-text, 30s audio clips

Hardware:

• GPU: AWS g4dn.xlarge (NVIDIA T4, 16GB VRAM, 4 vCPUs) — $0.526/hour on-demand

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