Introduction

GPUs accelerate ML inference but cost more than CPUs. For real-time trading, inference must be fast enough for latency constraints. Trade-off analysis quantifies: when is GPU investment justified by latency improvements? When is CPU sufficient? Cost-benefit analysis guides infrastructure choices.

Latency Benchmarking

Benchmark model inference latency on CPU and GPU hardware. Measure: inference time (milliseconds), throughput (predictions/second), power consumption. For different model sizes and batch sizes, GPU may be faster or slower. Small models may run faster on CPU (lower overhead). Large batch sizes favor GPU parallelization.

Cost Analysis

CPU cost: on-premise CPU servers or cloud CPU instances. GPU cost: GPU hardware purchases or cloud GPU instances (more expensive). Amortize hardware costs over usage. Calculate cost per inference ($/prediction). Break-even analysis: when does GPU latency benefit justify cost premium?

Decision Framework

For latency < 10ms requirement with large models: GPUs likely necessary. For latency < 100ms with small models: CPUs often sufficient. For very high throughput (millions predictions/second): GPUs justified. Balance latency, throughput, and cost constraints when deciding hardware.

Conclusion

Systematic GPU vs CPU cost-benefit analysis informs infrastructure decisions, optimizing performance/cost trade-offs.