Not just access.
Ownership.

The ownership shift starts now.

The next leap in Super Intelligence (SI) should be measured not only by what a model can do, but by who gets to own and operate it.

Builders and testers such as @ashxhart, @MiaAI_lab, @Tech2Wild and @niklaslenz_ai are making local inference’s possibilities visible. The opportunity is already taking shape: smaller weight files, better engines and shared recipes that make more of the hardware people own.

One concrete example: Tech2Wild’s published DeepSeek setup reports about 97 tokens per second for one user across four DGX Sparks, with its model pack, patches and run records documented. The repository credits TensorFold developers, quantization contributors and Mia-AiLab’s tensor layout. That is a specific community-reported result, not a universal speed claim.

More room. Faster access.

Memory is the other half. NVIDIA already offers DGX Spark with 128 GB of coherent unified memory and 273 GB/s bandwidth. Capacity determines what can fit; bandwidth helps determine how quickly the processor can use it. A terabyte of storage is not a terabyte of fast working memory.

Here is the forward-looking thesis behind Chad Hinkle’s X post: if memory constraints ease around 2029 and high-bandwidth, 1 TB unified-memory machines become affordable to far more people, today’s efficiency gains could compound into a much wider ownership opportunity. That date and configuration are a scenario—not a confirmed supply forecast or product announcement.

Own the capability.

The prize is practical control: running capable models on your hardware, keeping chosen data local, choosing the software and retaining access without depending on a provider for every request. Model licenses, usable tools, power and price still matter; memory alone does not establish general Super Intelligence.

Cloud services can remain useful. But they should be an option, not the only doorway.

Efficiency now. Accessible high-bandwidth memory next. Intelligence people can own—not merely rent.