Comment by antinomicus

No, that’s clearly not a goal of this project.

This is a learning tool. If you want a local model you are almost certainly better using something trained on far more compute. (Deepseek, Qwen, etc)

You can run a model locally on much less expensive hardware. It's training that requires the really big GPUs.

The 80 GB are for training with a batch size of 32 times 2048 tokens each. Since the model has only about 560M parameters, you could probably run it on CPU, if a bit slow.

I'd guess that this will output faster than the average reader can read, even while using only CPU inferencing on a modern-ish CPU.

The param count is small enough that even cheap (<$500) GPUs would work too.