Research & Insights
Our thinking on GPU-accelerated infrastructure, quantitative methods, and the engineering decisions behind the Ricche platform.
Why We Chose NVIDIA CUDA as Our Compute Foundation
The reasoning behind building our entire research pipeline on NVIDIA's GPU ecosystem — from data ingestion through training to inference — and why half-measures with CPU-GPU hybrid approaches fall short for financial ML workloads.
Read on DocsDesigning for Reproducibility: Lessons from Quantitative Research Infrastructure
How we are engineering experiment reproducibility into every layer of the platform — from deterministic data splits and versioned feature stores to auditable model lineage. The hidden cost of irreproducible research in quantitative finance.
Read on DocsThe Infrastructure Gap in Quantitative Finance
Why the gap between what top-tier quant firms can compute and what everyone else can access is widening — and how purpose-built GPU-accelerated research platforms can bridge it.
Read on Docs