How C++ and Android NDK Cut the Performance Cost of On-Device AI

As large language models like Gemini Nano run directly on Android devices, the performance overhead of managed runtimes like the JVM poses a serious challenge for developers building low-latency AI features. The core issue is that AI workloads require contiguous memory, precise byte alignment, and direct hardware access — conditions the Android Runtime's garbage collector cannot reliably provide. Developers are advised to implement AI computation kernels in C++ via Android's Native Development Kit, delegating entire tensor operations to native code rather than making frequent, costly Java Native Interface calls. Using direct ByteBuffers instead of standard Kotlin arrays also eliminates expensive memory copies between the managed and native layers. This architectural shift — from fine-grained JVM logic to coarse-grained native delegation — is presented as essential for achieving responsive, production-grade edge AI on Android.