NVIDIA DGX Spark and DGX Station power the latest open-source and frontier models from the desktop

NVIDIA DGX Spark and DGX Station Power the Latest Open-Source and Frontier Models From the Desktop

NVIDIA showed at CES that DGX Spark and DGX Station can run 100B to 1T-parameter models locally on deskside systems. The post cites a 35% average llama.cpp speedup, up to 70% NVFP4 compression, 775GB coherent memory on DGX Station, and a 250,000 token/sec pretraining demo. The real signal is the local dev loop: fine-tuning, inference, RAG, coding assistants, and robotics demos all target replacing some cloud iteration with deskside compute.

Why it matters: HKR-H/K/R all pass: the story pairs a strong desktop-scale hook with concrete specs and demo numbers, and it speaks directly to the local-vs-cloud workflow debate. Still, this is an NVIDIA product post and most performance evidence comes from vendor-run demos, so it stays at 75,.