Hardware✓ Mathematical
◆ The PatternVRAM management, TensorRT, and knowing when CPU is enough
GPUs accelerate inference through massive parallelism, but they're expensive and tricky to manage. TensorRT optimises models for NVIDIA GPUs with layer fusion and kernel auto-tuning. Triton Inference Server handles multi-model scheduling and dynamic batching on GPU.
// Interactive — GPU vs CPU throughput comparison
Model Size (M params)100M
| Approach | Speedup | Tradeoff |
|---|---|---|
| TensorRT FP16 | 2–4× | Slight accuracy loss, compile time |
| TensorRT INT8 | 3–6× | Needs calibration dataset |
| Multi-model GPU | Better utilisation | Memory contention |
| CPU (small models) | Baseline | Often sufficient for <10M params |
Cost rule of thumb: If your model is under 10M parameters and doesn't process images/audio, benchmark CPU first. GPU inference makes sense when you're throughput-bound, not latency-bound.
Pattern bridge: GPU vs CPU is a cost governance decision — the same risk/reward calculus as risk appetite in portfolio construction. Spend more only when the marginal return justifies it.