
Monte Carlo Acceleration
History of Quante Carlo
2007 - NVIDIA releases CUDA
CUDA allows the GPU to be used as a general computing platform. This allows high performance computing at a fraction of the cost of traditional grid computing. It is only getting faster and chaper.
2008 - Financial Crisis
The financial sector uses some of the most advanced technology in the world to calculate high precision risk extremely quickly. CUDA is now deployed in financial institutions all of the world.
2020 - Optimization
CUDA has been increasing in popularity but due to the parallelization model, it has had limited uses in optimization. One recent use case that is very important to AI is Hyperparameter Tuning. These models cost over $1B each to build and train. Making sure they have the optimal parameters is key.
Prompt and Text Optimization
In many ways, Agents are defined by their prompts. Prompt defined Agents are easy to orchestrate. You simply orchestrate one model with many different prompts. This allows complex and scalable Agent Orchestration. Quante Carlo allows prompts to be highly tuned to specific use cases.

Seamless Integration with Your AI Environment
Prior state of the art, Quasi-Monte Carlo uses symmetry theory to efficiently sample a search space.
Quante Carlo uses proprietary, unpublished algorithms deployed on modern, efficient hardware to perform Monte Carlo calculations 1000s of times faster than the second best technology.
​
Deployed as an API, users can simply call Quante Carlo as a Python API and our servers do the rest.