Tuning llama.cpp Performance on NVIDIA DGX Spark

I compare llama.cpp build and server settings on the NVIDIA DGX Spark, focusing on flags that affect prompt processing, token generation, and model loading. Using benchmark runs and a server example with Nemotron and gpt-oss-120b, I show how to evaluate these options in practice.

- llama-bench comparisons: flash attention and varying batch sizes
- Server configuration: reasoning format, memory mapping, and performance checks

This is aimed at llama.cpp users running models on DGX Spark who want to test practical configuration changes. Apply the settings to your own models and backend, checking batch-size behavior since it can vary by setup; the discussion does not cover a general guide to building llama.cpp across platforms.