# Grace-Blackwell GB10 Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/grace-blackwell-gb10
> Markdown URL: https://aitinkerers.org/technologies/grace-blackwell-gb10.md
> Technology record last updated: 2026-03-05T15:46:27Z
> Generated: 2026-09-23T01:48:09Z

The GB100 architecture integrates two Blackwell GPUs with a Grace CPU via a 900GB/s NVLink-C2C interconnect to deliver 20 petaflops of AI performance.

NVIDIA's Grace-Blackwell GB200 superchip marks a massive jump in compute density (30x faster LLM inference compared to H100). It pairs the ARM-based Grace CPU with two high-performance Blackwell GPUs, utilizing a second-generation transformer engine to handle 4-bit floating point (FP4) precision. This hardware is the backbone of the NVL72 rack system: a liquid-cooled powerhouse that acts as a single 72-GPU node with 130TB/s of aggregate bandwidth. By slashing energy consumption by 25x and reducing costs, it provides the massive scale required for trillion-parameter model training and real-time deployment.

- Official technology site: https://www.nvidia.com/en-us/data-center/gb200-nvl72/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [From Laptop to Supercluster: The New Era of Personal AI Supercomputing](https://paris.aitinkerers.org/talks/rsvp_tpKMaxLqXkQ)

AI development is shifting rapidly from centralized cloud infrastructure to powerful local AI systems that enable developers to build, test, and run large models directly on their desks. Lenovo’s new AI workstation portfolio powered by NVIDIA Blackwell and Grace-Blackwell architectures introduces a new class of systems—from compact personal AI appliances to multi-GPU developer workstations capable of running hundreds-billion-parameter models locally. This talk explores how AI development is moving closer to the developer, enabling faster iteration, lower cloud costs, and new experimentation workflows. We will walk through the new Lenovo AI workstation stack, including the ThinkStation PGX (Grace-Blackwell GB10 superchip) and Blackwell GPU developer workstations, and explain how these systems scale from personal AI experimentation to enterprise-grade model development. Questions the Talk Will Answer What does “personal AI supercomputing” actually mean for developers? How large of an AI model can realistically run locally on a workstation today? When should developers use local AI vs cloud GPU clusters? How do systems like PGX, P3, P5, P7, and PX map to different AI workloads? What does the Grace-Blackwell architecture change in AI workstation design? How can developers prototype and iterate faster using local AI hardware?

- Event context: High-Performance Local AI Development: Kick-off ThinkStation PGX — 2026-03-17 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_tpKMaxLqXkQ

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