NV-Tesseract is a family of deep‐learning models designed specifically for time‐series data (i.e., sequences of values over time) rather than just static tabular or image data. presentation below is by Weiji Chen, AI/ML Engineer, NVIDIA

Evolving to Tesseract 2.0

Then Tesseract 2.0 integrate rest of NVIDIA technologies, such as RAPIDs(a python c++ libraries leveraging CUDA-X, cuDF, cuPy, RMM to accelerate computation greatly), next

NV‑Tesseract model family by NVIDIA is not yet generally available for open use in the sense of a freely downloadable open-source package or model weights. You have to get license/agreement from NVIDIA to use DGX Cloud to test it out (DGX = NVIDIA’s own AI supercomputer + managed service layer for training, fine-tuning, and deploying large models.)

It’s possible that some quant shops are experimenting with NV-Tesseract (or plan to) given its targeted time-series & financial claims. But as of now, it’s an emerging technology and hence opportunities are big.