NVIDIA's 748GB Desktop Makes Local AI INSANELY Great (Runs 70B, No Cloud)

NVIDIA's new DGX Station puts 748GB of unified memory on a desktop enough to run a 70B local AI model at home with room to spare. No cloud, no 24GB VRAM wall: LM Studio, Ollama, DGX Spark, and what "unified memory" really unlocks. NVIDIA just announced the DGX Station, a 748GB desktop AI machine built on the GB300 Grace Blackwell Ultra superchip. In this breakdown I separate what's real from what's hype: why memory — not speed — was always the thing stopping you from running big models locally, how 748GB of coherent unified memory finally lets a 70B model fit, the truth behind the "trillion-parameter" claim, the real $90K–$115K price, and the cheaper ladder below it (DGX Spark at ~$4K and a maxed Mac Studio). Then the cloud math that actually justifies a $100K box — and the one caveat nobody's telling you: the Windows version isn't out until Q4. ⏱️ CHAPTERS 0:00 NVIDIA put 748GB on a desktop 0:30 Where this comes from (NVIDIA newsroom) 0:50 Inside the DGX Station — GB300 Grace Blackwell Ultra 1:25 One giant shared memory pool (252GB + 496GB = 748GB) 2:00 The real wall: a 70B model needs ~140GB 2:45 The "trillion parameter" asterisk 3:30 The price: $90K–$115K (and who sells it) 4:05 The ladder: DGX Spark vs Mac Studio 4:55 The cloud math — ~2-month payback 5:40 Linux now, Windows Q4 (the catch) 6:05 Which machine goes on your desk? 6:30 Mac Studio, DGX Spark, or waiting it out? 🔗 SOURCES NVIDIA newsroom: https://nvidianews.nvidia.com/news/nv... NVIDIA DGX Station: https://www.nvidia.com/en-us/products... NVIDIA DGX Spark: https://www.nvidia.com/en-us/products... LM Studio: https://lmstudio.ai Ollama: https://ollama.com #localai #nvidiadgx #dgxstation #lmstudio #ollama #ai #dgxspark