# GPUs Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/gpus
> Markdown URL: https://aitinkerers.org/technologies/gpus.md
> Technology record last updated: 2026-03-04T06:10:37Z
> Generated: 2026-08-25T00:35:40Z

A parallel processing powerhouse: GPUs (Graphics Processing Units) accelerate high-demand workloads like 4K gaming, deep learning AI (e.g., LLMs), and scientific simulation via thousands of specialized cores.

The GPU is a specialized electronic circuit designed for massive parallel processing, fundamentally shifting from its original role in 3D graphics rendering to general-purpose computing (GPGPU). Unlike a CPU's few powerful cores, a GPU employs thousands of smaller, efficient cores (like NVIDIA's CUDA cores) to handle simultaneous calculations, which is critical for modern workloads. This architecture drives performance in high-fidelity gaming (4K/VR), video editing, and especially in AI/Machine Learning: the Tensor Cores, for instance, deliver significant FLOPS for training large language models (LLMs). GPUs are now the core engine for high-performance computing (HPC) and data centers.

- Official technology site: https://en.wikipedia.org/wiki/Graphics_processing_unit
- Public AI Tinkerers demos and talks: 5
- 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

### [Mapping AI Companies](https://boston.aitinkerers.org/talks/rsvp_oj3AYSHa9Nc)

We know about the impact of GPUs on AI. Visual cortex is our brain's GPU. It's been empirically proven that people who can visualize math, solve problems faster. So, we're all trying to make sense of a vast number of companies and projects popping up in AI. But search gives us 1-dimensional lists. We want spaces, landscapes, galaxies of projects to better understand what's going on. I'll show a 1-day hack we did to map all YC companies into a galaxy, and the code is on GitHub. We used LLM embeddings to both facilitate querying as well as visualization. Let's build galaxy maps!

- Event context: AI Tinkerers September Meetup Sponsored by BasisTech — 2024-09-23 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_oj3AYSHa9Nc

### [Building Semiconductors in my Apartment](https://toronto.aitinkerers.org/talks/rsvp_ySgNtzKHHaM)

Because the future of tech is semiconductors—but the barrier to entry is massive. 🏗️ So, we are starting a design team Waterloo Hacker Fab building low-cost DIY equipment 🛠️ to manufacture semiconductors devices. 💰 With $16,000 in funding already secured, we've just completed the first step of semiconductor manufacturing – Patterning and Coating. Now, we want to take it to the next level, and we’re looking for passionate Waterloo students interested learning more about semiconductors and building this out.

- Event context: AI Tinkerers - Toronto - September 2024 Meetup — 2024-09-20 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_ySgNtzKHHaM

### [Large Scale AI/ML Inference with GPUs](https://boston.aitinkerers.org/talks/rsvp_0FT2VStA-DM)

Most production AI models using deep learning will be performing computational forward passes for inference. This demo will go into the steps you would take to accelerate and optimize inference on models like LLMs with the power of GPUs and batch processing. We may also be showing the results via a vector database.

- Event context: August 2024 Meetup at Æthos — 2024-08-26 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_0FT2VStA-DM

### [Own Your AI - Deploying a Consumer GPU Fleet in Production](https://chicago.aitinkerers.org/talks/rsvp_oTZLGL_T6Eg)

Datacenter GPUs are expensive. Hosting at home is doable, but you deal with power, bandwidth, network availability, and space. How do you scale a GPU homelab in production? Here are all the tactical insights I've learned from financing & importing hardware, negotiating contracts, planning, and operating rack inside of Digital Realty for the purpose of hosting AI models.

- Event context: AI Tinkerers Chicago June Meetup — 2024-06-18 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_oTZLGL_T6Eg

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