Running deep learning networks on localized hardware reduces network costs and latency, but introduces compute bottlenecks. This benchmark evaluates edge TPUs, NPUs, and GPUs against classic cloud servers.
Hardware Benchmarks
Edge silicon provides surprisingly fast execution for quantized models (INT8). By avoiding round-trip times to remote cloud centers, edge-side inference achieves sub-10ms latency for vision tasks.