NVIDIA'S 748GB Ram Desktop Makes Local AI INSANELY Good
LM Studio local AI just changed: NVIDIA's DGX Station packs 748GB unified memory to run 70B models in full precision, no cloud needed. LM Studio and Ollama finally lose their asterisk, NVIDIA's DGX Station at Computex 2026 lands 748GB of coherent unified memory in a single deskside tower, enough to load a full-precision 70B model with room to spare, no quantization required, no cloud offload. Announced by Jensen Huang at GTC Taipei on May 31, 2026, the machine is built around the GB300 Grace Blackwell Ultra Desktop Superchip: a 72-core ARM Grace CPU fused to a Blackwell Ultra GPU via NVLink-C2C at 900 GB/s. The memory pool splits into 252GB HBM3e (7.1 TB/s GPU-side) and 496GB LPDDR5X (CPU-side), both fully coherent, one address space, zero explicit copies. Compute tops out at 20 petaFLOPS FP4. NVIDIA doesn't sell a Founders Edition; OEM partners ASUS, Dell, HP, MSI, and others handle that, with real-world pricing landing between roughly $85K and $115K (the MSI XpertStation WS300 lists at $96,995.99 on CDW). The video also covers NVIDIA's DGX Spark (128GB, ~$4,700) as the genuine prosumer entry point, and gives an honest head-to-head with the Mac Studio M5 Ultra, which still holds the value crown for a solo developer running mid-size models. The trillion-parameter claim gets a reality check, it's technically true only with aggressive 4-bit quantization, not full-precision weights. The cloud ROI math is real: at ~$98/hour for a comparable AWS p5 instance, the hardware pays for itself in roughly two months of sustained workload. The DGX Station for Windows (WSL-based) is flagged as a Q4 2026 promise, not a shipping product. RTX Spark, NVIDIA's MediaTek-partnered consumer AI PC chip, rounds out the roadmap alongside a three-generation plan through Rubin and Rosa Feynman. For individual builders and small teams deciding between local AI options, this is a practical breakdown of which box on the NVIDIA ladder actually makes sense for their workload. Chapters: 0:00 Intro 0:15 What Jensen actually unveiled 1:12 Why unified memory is the whole story 2:31 The trillion-parameter asterisk 3:25 The price, who it's for, and how to choose 5:25 The cloud math that justifies the big box 6:40 The bigger play: NVIDIA wants the whole PC Tools & resources mentioned: - LM Studio: https://lmstudio.ai - Ollama: https://ollama.com - NVIDIA DGX Station: https://www.nvidia.com/en-us/products/workstations/dgx-station/ - NVIDIA DGX Station for Windows: https://www.nvidia.com/en-us/products/workstations/dgx-station-for-windows/ - NVIDIA DGX Spark: https://www.nvidia.com/en-us/products/workstations/dgx-spark/ About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #localai #nvidiadgx #lmstudio #computex2026 #agenticai
