Performance

Python is the default language for inference servers, and for good reason: PyTorch, HuggingFace, and most ML tooling are Python-first. But if the rest of your stack is Go, you end up with a Python sidecar just to call model.forward(). That sidecar needs its own container, its own health checks, its own deployment pipeline, and its own debugging story.

Two functions. Nearly identical code. One puts your variable on the stack; the other sends it to the heap, adding GC pressure every time it runs. You cannot tell which is which by reading the code — you need to ask the compiler.

You set GOMAXPROCS=8 on a 16-core machine because you read that matching CPU count improves throughput. Your CPU-bound service gets slower. The goroutine count on the dashboard rises steadily. Nothing in the logs explains it, and the Go documentation does not mention this case.