FPGA vs GPU for AI Inference: Latency, Power and Cost Compared
Short answer: GPUs win on raw throughput and development speed. FPGAs win when you need deterministic, ultra-low latency, tight power budgets, or custom numeric precision that neither GPU nor ASIC supports.
Where FPGAs genuinely win
- Deterministic latency. An FPGA implements a fixed data path. Latency is bounded and repeatable, which matters in industrial control, high-frequency trading and closed-loop robotics — places where a 99th-percentile spike is unacceptable.
- Power efficiency at low batch. With no instruction fetch, cache hierarchy or OS scheduling, a well-designed FPGA pipeline can beat a GPU on performance-per-watt for small-batch, streaming workloads.
- Custom precision. INT4, INT2, binary networks or unusual bit widths are straightforward on an FPGA and awkward elsewhere.
- Interface flexibility. Direct sensor or high-speed ADC attachment without a host round-trip.
Where GPUs win
- Throughput. Large-batch inference strongly favours GPUs.
- Time to market. CUDA and TensorRT turn a trained model into a running service in hours. Comparable FPGA work is a hardware engineering project.
- Model churn. If your model changes monthly, re-synthesising an FPGA bitstream each time is expensive.
Comparison
| Criterion | FPGA | GPU |
| Peak throughput | Lower | Higher |
| Latency consistency | Deterministic | Variable |
| Performance per watt (small batch) | Often better | Lower |
| Development cost | High | Low |
| Precision flexibility | Very high | Limited to supported types |
| Model update cycle | Re-synthesis | Re-deploy weights |
The hybrid pattern
A common production architecture uses an FPGA for deterministic pre-processing and I/O (sensor ingest, synchronisation, triggering) and a GPU or NPU for the heavy inference stage. This keeps the latency-critical path deterministic while retaining a conventional model deployment flow.
Frequently asked
Is FPGA cheaper than GPU? Rarely on unit cost alone for equivalent throughput. FPGAs pay off on total system cost — power, cooling, enclosure size and reliability.
Do I need HDL skills? Traditionally yes, though HLS (C/C++ to RTL) and vendor AI toolchains have lowered the barrier considerably.
Which Puzhi FPGA board should I start with? For learning and prototyping an Artix-7 based development board is the usual entry point; move to a larger device or a SoM when your design outgrows it. See our FPGA vs GPU guide.
