Engineering CPU and accelerator-backed ML inference from JVM applications: choosing in-process versus remote serving, bounding native sessions and predictors, coordinating engine and request parallelism, batching under a latency deadline, reusing direct buffers, warming deployments and diagnosing native memory outside NMT. Use when DJL, ONNX Runtime or another native inference engine loses throughput as concurrency rises, leaks RSS, overloads a model pool or needs graceful degradation. Training and choosing model quality targets are outside scope.
