Cure51 Achieves 17x Speedup in Genomic Analysis Using NVIDIA DGX Cloud
Cure51 · France
Cure51, a Paris-based Sofinnova portfolio company, achieved a 17x speedup in genomic analysis and 2x cost savings by shifting workloads to NVIDIA Parabricks running on NVIDIA DGX Cloud, accessed through the DGX Cloud Lepton program for top European healthcare and life sciences venture-backed startups, which offers access to NVIDIA H100 GPU nodes and white-glove support to help scale into markets requiring sovereign, localized compute.
Overview
Cure51, a Paris-based Sofinnova portfolio company, achieved a 17x speedup in genomic analysis and 2x cost savings by shifting workloads to NVIDIA Parabricks running on NVIDIA DGX Cloud, accessed through the DGX Cloud Lepton program for top European healthcare and life sciences venture-backed startups, which offers access to NVIDIA H100 GPU nodes and white-glove support to help scale into markets requiring sovereign, localized compute.
The challenge
Cure51 needed to scale genomic analysis workloads and access markets requiring sovereign, localized compute.
The solution
Cure51 shifted genomic analysis workloads to NVIDIA Parabricks running on NVIDIA DGX Cloud, accessed through the DGX Cloud Lepton program, which offers top European healthcare and life sciences venture-backed startups selected via VC firms like Sofinnova early access to NVIDIA H100 GPU nodes plus white-glove support to help them scale into new markets requiring sovereign, localized compute.
Reported business value
Cure51 achieved a 17x speedup in genomic analysis and 2x cost savings by shifting workloads to NVIDIA Parabricks running on DGX Cloud.
Sources
Open any source and check the claim yourself — that is the point of the register.
Other healthcare entries in the register.
Novo Nordisk builds AI drug discovery platform on Azure with Microsoft Research
Novo Nordisk partnered with Microsoft Research to build an AI platform on Azure AI and data stacks spanning regulatory affairs, early research, drug discovery and trial design, using Azure OpenAI Service, Azure Cosmos DB and Azure Kubernetes Service, with Power BI and Power Apps for collaboration. The platform includes a copilot for researchers, shared reasoning-chain templates, and governance/auditing of how data and models are used. The teams published early results on predictive AI models for cardiovascular disease risk detection, including an algorithm that Novo Nordisk says predicts patients' cardiovascular risk better than the best clinical standards, drawing on more than 100 years of insulin research data.
CDPHP modernizes infrastructure and improves medical data extraction with AWS AI/ML
CDPHP, a not-for-profit health plan serving 400,000 members in Upstate New York, used AWS services including Amazon Comprehend Medical, Amazon Textract, and Amazon SageMaker to automate its data processing pipeline for unstructured medical records and health data. The organization processed over seven million records during initial migration and now processes 3,000 electronic health records weekly. CDPHP achieved a 60% improvement in overall efficiency and reduced HEDIS report generation from 4-5 days (three data scientists) to two reports produced daily.
Mayo Clinic deploys NVIDIA DGX SuperPOD to accelerate pathology foundation models
Mayo Clinic deployed an NVIDIA DGX SuperPOD with NVIDIA DGX B200 systems to support foundation model development for pathomics, drug discovery and precision medicine. In partnership with Aignostics, Mayo Clinic built the Atlas pathology foundation model, trained on more than 1.2 million histopathology whole-slide images. The new infrastructure is reducing four weeks of pathology slide analysis work to one week.
Myriad Genetics speeds document processing with AWS GenAI Intelligent Document Processing Accelerator
Myriad Genetics partnered with the AWS Generative AI Innovation Center to replace an Amazon Textract/Comprehend pipeline with Amazon Bedrock foundation models (Nova Pro for classification, Nova Premier for extraction) using the open-source GenAI IDP Accelerator. Document classification accuracy rose from 94% to 98%, classification cost per page fell 77% (3.1 cents to 0.7 cents), and classification time fell 80% (8.5 minutes to 1.5 minutes per document). Automated key information extraction reached 90% accuracy matching the manual baseline, with a projected $132K in annual savings and 300 hours saved monthly across 9,000 prior authorizations in the Women's Health unit alone.
Was this helpful?
Your feedback helps us improve our use case database