A Gentle Introduction to XGBoost for Applied Machine Learning - MachineLearningMastery.com

"Why are you benchmarking DGX Spark? It's a training box."Yeah. Low bandwidth, but 128GB of unified memory is just sitting there. Plenty of room to optimize.DGX Spark + Qwen3.6 27B. Four backend/quant combos:🔴 llama.cpp + UD_Q4_K_XL> 11.0 tok/s (baseline), TTFT 297ms🟢 llama.cpp + DFlash> 20.4 tok/s (peaks at 97 tok/s), TTFT 320ms🟡 vLLM FP8 + MTP> 13.1 tok/s, TTFT 540ms🟣 vLLM NVFP4 + MTP> 24.2 tok/s, TTFT 376msNVFP4+MTP is the winner for me, rock stable around 24 tok/s, no wild swings.DFlash

dgx spark learning log.trying to understand how far small local agent models can go before we point them at real benchmarks like webworld.this run used:- hardware: nvidia dgx spark / gb10- model: DJLougen/Harmonic-Hermes-9B-GGUF, Q5_K_M- runtime: llama.cpp llama-server, local OpenAI compatible endpoint- model lineage: qwen3.5 architecture; Harmonic-Hermes-9B by @DJLougen , GGUF quant metadata says quantized by @UnslothAI - test harness: a tiny local python repo with pytest- safe tools exposed to