FPGA vs GPU for AI Acceleration: When to Choose Which
Two different tools
A GPU (Jetson, RK3588 NPU) runs trained neural nets efficiently via CUDA/TensorRT/RKNN. An FPGA / Zynq (Xilinx Zynq-7000 / UltraScale+) does deterministic, low-latency hardware acceleration and interfacing that GPUs are poor at — high-speed I/O, real-time signal processing, custom pipelines.
Comparison
| Dimension | Xilinx Zynq FPGA | GPU (Jetson / RK3588) |
|---|---|---|
| Best at | Real-time DSP, custom logic, deterministic latency | Parallel neural-net inference, CUDA/TensorRT |
| Latency | Nanosecond-level, deterministic | Milliseconds, software-scheduled |
| Power | Very low (mW–W) | Higher (W–tens of W) |
| AI inference | Implemented as custom IP (less turnkey) | Turnkey via ML frameworks |
| I/O flexibility | Highest (LVDS, gigabit serial, custom) | Fixed interfaces |
| Learning curve | HDL / Vivado (steeper) | C++ / Python (gentler) |
| Price | $139–$499 (Puzhi boards) | $269–$3979 |
Zynq / FPGA boards in stock (Puzhi)
- Puzhi PZ7020S-SOM ZYNQ-7000 ($169.99) — ZYNQ-7000 XC7Z020 core board.
- Puzhi ZYNQ UltraScale+ ZU2CG ($499.90) — ZYNQ UltraScale+ ZU2CG.
- Puzhi PZ7020-StarLite Dev Board ($209.99) — PZ7020-StarLite development board.
GPU / SoC boards in stock
- NVIDIA Jetson Orin Nano Super Dev Kit ($599.00) — NVIDIA Jetson Orin Nano Super.
- Station M3 ARM Mini PC – RK3588S ($349.00) — RK3588S mini PC.
- AIBOX-3588 RK3588 Edge Fanless PC ($449.00) — RK3588 edge PC.
How to choose
Use an FPGA/Zynq when you need deterministic real-time control, exotic I/O, or to preprocess sensor data before inference. Use a GPU/SoC when the job is standard neural-net inference. Many production systems pair both: Zynq for capture/preprocess, Jetson/RK3588 for inference.
Frequently Asked Questions
Can an FPGA run neural networks?
Yes, but as custom RTL/IP rather than off-the-shelf frameworks. FPGAs excel at the pre/post-processing and deterministic parts of a pipeline more than at competing with GPUs on raw TOPS.
Is Zynq harder to develop than Jetson?
Yes — Zynq development uses Vivado/HDL and is more involved than Python/CUDA on Jetson. Budget extra engineering time or use vendor reference designs.
Do you carry both?
Ai-Paipai stocks Puzhi Zynq/FPGA boards alongside NVIDIA Jetson and Firefly RK3588 — so you can prototype both sides of a hybrid pipeline from one supplier.
More comparisons: RK3588 vs NVIDIA Jetson · RK3588 vs RK3576 · RK3588 vs RK3566