The Full Stack

At GTC Taipei, NVIDIA closed the last gap in a silicon stack that runs from trillion-parameter racks to consumer laptops.

Jensen Huang opened GTC Taipei last Monday at the Taipei Music Center alongside Computex and announced three products in rapid succession: a next-generation data-center platform in full production, a consumer PC superchip, and an open foundation model for physical AI. Each targets a different market, but all three share the same Blackwell silicon foundation and the CUDA software ecosystem that runs on it.

Bare-metal inference

Vera Rubin, the platform pairing NVIDIA’s in-house Vera CPU with its Rubin GPU, has entered full production. The NVL72 racks bundle Rubin GPUs, Vera CPUs, Groq 3 LPX inference trays, Spectrum-X networking with co-packaged optics, and BlueField-4 storage controllers into systems designed from the silicon outward, an approach NVIDIA calls “extreme co-design.” NVIDIA claims that the resulting system delivers inference at one-tenth the cost of Blackwell, and Huang called it the lowest per-token cost in the industry.

Huang projected $1 trillion in cumulative orders for the Blackwell and Rubin families by 2027. Samsung Electronics closed up over 10% after Huang named it as an HBM4 memory supplier alongside SK Hynix and Micron, and eight major cloud providers, including AWS, Google Cloud, and Azure, will offer Vera Rubin systems in the second half of this year. The supply chain backing the ramp concentrates almost entirely in Taiwan, where NVIDIA now counts 150 partner factories, a concentration that carries both production efficiency and geopolitical exposure.

A new contender

RTX Spark, a 70-billion-transistor superchip on TSMC’s 3nm process that NVIDIA designed in partnership with MediaTek, puts the company into the consumer PC market for the first time in its history. The chip pairs a Blackwell GPU with 6,144 CUDA cores against a 20-core Grace CPU connected through NVLink-C2C, with up to 128GB of unified memory and one petaflop of AI performance. Intel’s stock fell roughly 4.7% the day after the announcement. Huang framed the move as a generational shift in which the PC goes from “tool to teammate,” as he put it, with AI agents running locally through a new runtime called NVIDIA OpenShell that enforces user-defined privacy and security policies.

Microsoft Surface, Dell, HP, ASUS, Lenovo, and MSI have confirmed RTX Spark designs, with over thirty laptops and ten desktops due this fall. Adobe disclosed that it is rearchitecting Photoshop and Premiere Pro from the ground up for the platform. The full CUDA ecosystem runs on the chip, which means that code written for data-center Blackwell GPUs executes on the laptop without recompilation. NVIDIA claims that the chip can handle 120-billion-parameter models with million-token context windows entirely on-device.

Software across the stack

NVIDIA also launched Cosmos 3, an open foundation model for physical AI built on a mixture-of-transformers architecture that pairs a reasoning transformer with a generation transformer. The model handles text, image, video, ambient sound, and robot action trajectories in a single system, targeting robotics and autonomous-vehicle developers who currently stitch together separate models for each modality. Open weights went up on Hugging Face the same day, and a Cosmos Coalition of partner labs, including Agile Robots, Black Forest Labs, Runway, and Skild AI, launched alongside it.

A desktop workstation called DGX Station for Windows fills the space between data center and laptop. The machine pairs a Blackwell Ultra GPU with a 72-core Grace CPU and up to 748GB of coherent memory, enough to run trillion-parameter models locally. Cosmos 3 trains on Vera Rubin infrastructure and deploys across DGX Station and Spark-class hardware, with the same CUDA libraries and TensorRT optimization serving every tier.

The complete package

Whether the consumer market follows the architecture remains to be seen. Windows on Arm has struggled to attract mainstream adoption, and the gap between NVIDIA’s on-stage benchmarks and real-world sustained performance in a slim laptop chassis will depend on thermal engineering that varies by manufacturer. Apple spans a similar architectural range with its M-series silicon, but exclusively within its own operating system and without reaching the data-center training tier. AMD and Intel compete at individual layers without the top-to-bottom integration. NVIDIA’s Computex week put every piece of a unified stack into production or onto a confirmed shipping timeline, completing the architectural argument before the market has rendered its verdict.