Some smaller open-weights AI language models (such as Llama 3.1 70B, with 70 billion parameters) and various AI image-synthesis models like Flux.1 dev (12 billion parameters) could probably run comfortably on Project DIGITS, but larger open models like Llama 3.1 405B, with 405 billion parameters, may not. Given the recent explosion of smaller AI models, a creative developer could likely run quite a few interesting models on the unit.
DIGITS’ 128GB of unified RAM is notable because a high-power consumer GPU like the RTX 4090 has only 24GB of VRAM. Memory serves as a hard limit on AI model parameter size, and more memory makes room for running larger local AI models.
Stable Diffusion is a latent diffusion model that generates AI images from text. Instead of operating in the high-dimensional image space, it first compresses the image into the latent space.
Stable Diffusion belongs to a class of deep learning models called diffusion models. They are generative models, meaning they are designed to generate new data similar to what they have seen in training. In the case of Stable Diffusion, the data are images.
Why is it called the diffusion model? Because its math looks very much like diffusion in physics. Let’s go through the idea.
