There are about 900 estimated CUDA libraries from NVIDIA. I will select the ones relevant to my needs.

Library	Purpose	Robotics Use

cuBLAS

GPU-accelerated linear algebra (matrix mult, LU, QR)

Rigid-body dynamics, transforms

cuSOLVER

Linear system & eigen decomposition

Inverse kinematics, least squares

cuSPARSE / cuDSS

Sparse matrices & solvers

Large Jacobian systems, graph optimization

cuRAND

Random number generation

Monte Carlo simulations, sensor noise

cuFFT

Fast Fourier Transform

Vision, signal processing, lidar

cuTENSOR

Tensor contractions (multi-dimensional arrays)

Complex physics simulations

CUDA Graphs API

Efficient task scheduling

Real-time control, multi-step planning

Thrust

STL-like GPU parallel library

High-level vector/matrix ops in C++

cuBLAS / cuSOLVER	Matrix algebra	Covariance, regression, PCA
cuRAND	Random sequences	Monte Carlo pricing, risk simulation
cuDF / cuML (RAPIDS)	GPU DataFrame & ML toolkit	Replace pandas/sklearn at GPU speed
cuGraph	Graph analytics	Index constituent networks, dependency graphs
TensorRT / cuTENSOR	Model inference optimization	Accelerate AI models for portfolio analytics
cuQuantum	Tensor simulation	(Future) modeling complex probabilistic systems

Once master these libraries, one can try to build GPU kernels on her domain, by the way, GPU kernel is a function designed to run on a graphics processing unit (GPU), usually written in CUDA C/C++ or sometimes with Python tools like Numba or CuPy. It’s highly parallel and operates on many data elements at once, maximizing the speedups that GPUs provide for large, data-parallel tasks (like matrix multiplication or vector addition). GPU kernels are launched and controlled from the host (CPU), but their code executes massively in parallel across GPU cores. It’s conceptually different than Python kernel.

Even if you’re using the same CUDA libraries from NVIDIA, you can totally stand out or whip up better GPU kernels just by really getting into the math, CUDA, and what you’re working on. This know-how helps you write functions or kernels that do a better job with specific tasks, giving you a sweet edge in the market. Plus, NVIDIA has this inception unit that’s all about putting its offerings exactly where they’re needed the most.

Existing index providers like S&P, MSCI, and Solactive are undeniably data-rich yet sluggish. Their backtesting systems are inefficient, CPU-bound, and rigid. Now, picture a next-gen GPU-driven Index Intelligence Platform: By leveraging CUDA/cuDF/cuBLAS, one could calculate index performance, volatility, and optimization an astounding 100× faster; empowering users to create indices that adapt seamlessly to data or macro shifts. Additionally, integrating NVIDIA NIM or TensorRT will enable insightful analysis of company fundamentals or news as key signals.

Existing SDKs (NVIDIA Isaac, ROS2, Figure, Boston Dynamics) focus on simulation + hardware control. You could focus on the software intelligence layer.

Implement kinematics, SLAM, and trajectory planning on GPUs with cuBLAS/cuSOLVER. Connect to NVIDIA Omniverse for automatic creation of AI twins for real robots. Develop a layer that allows different robot types (arms, drones, AGVs) to be programmed with the same API. Use AI models to adjust control parameters from simulation to the real world. Outcome: You become the “CUDA for Robotics Intelligence” — a software core that others can build upon.