Local Inference on Thede Technologies

A general-purpose MoE multimodal beat every dedicated vision model on my father's handwriting

Thu, 23 Apr 2026 00:00:00 +0000

For context: I’ve been transcribing a multi-generational archive of handwritten family letters on my own hardware. The first two posts covered why I’m doing this at all and how to set it up on your own machine . This post is the surprising finding — the one I didn’t expect going in.

I assumed the right tool for a vision task was a vision model. That’s the obvious reach. If you’re reading handwriting, you reach for something labeled “VL.” If you can find one fine-tuned for OCR and handwriting specifically, even better.

Running real models locally on a Mac Studio that isn't new anymore

Thu, 23 Apr 2026 00:00:00 +0000

I have a multi-generational archive of handwritten family letters sitting in my house, and I wanted to read it without sending the private content to a cloud provider. The first post in this series is the why. This one is the how — on the specific hardware I already own, with the specific software I landed on.

My Mac Studio is almost four years old now, but it has 64 GB of RAM and it has an M1 Max chip. It has proven itself to be quite capable of running large language models locally for personal benefit. It helps with maintaining privacy, and it also is much less expensive than leveraging any cloud provider.

What family archives are for, now that the AI can read them

Thu, 23 Apr 2026 00:00:00 +0000

I have three collections of letters sitting in my house.

The oldest is a stack from my great-grandparents, written between 1910 and 1913. The second is from my grandparents, written during World War II. The third is from my father — letters he wrote from the moment I was born until the moment his mother died.

There are other things too. Memoirs. Loose documents. A family record that shows up in more places than I can keep track of. But those three collections are the weight of it. Three generations, each one writing to the next, each one leaving behind something I’ve never fully read.