NVIDIA Clara is a collection of developer tools specifically for healthcare, built on the NVIDIA Compute Platform, that accelerate data collection, analysis and integration.

Across the healthcare industry, the number of software-defined solutions has grown exponentially, driving efficiencies through automation and generating large volumes of digital data. Artificial intelligence has made it possible to integrate and analyse this data in new ways to gain deeper insights. NVIDIA Clara aims to give medical imaging and genomics developers access to technological advances in hardware and software to accelerate the future of medicine.

From automating workflows to improving processing speed and image quality, medical imaging developers around the world are discovering numerous ways to use AI to help doctors detect and diagnose disease. With the Clara platform, they are harnessing AI to transform healthcare workflows.


Clara Medical Imaging is a collection of developer toolkits built on the NVIDIA Compute platform and aimed at accelerating computation, artificial intelligence and advanced visualisation. The medical imaging industry is undergoing a transformation. A decade ago, the first applications to take advantage of GPU computing were imaging and signal processing applications.

Today, GPUs are found in almost all imaging modalities, including CT, MRI/MRI, X-ray and ultrasound, bringing more computing power to the edge devices. Deep learning research in medical imaging is also booming, with more efficient and improved approaches being developed to enable AI-powered workflows. Today, most of this AI research is conducted in isolation and with limited datasets, which can lead to oversimplified models. Even when a fully validated model is available, it is challenging to deploy the algorithm in a local environment. With the latest release of Clara AI for Medical Imaging, data scientists, researchers and software developers now have the necessary tools, APIs and development frameworks to train and deploy AI workflows.


The NVIDIA Clara AI technology stack includes system software libraries that form the foundation for GPU computing, as well as abstracted software tools, containers and workflow definition pipelines that enable data scientists and medical imaging developers to create and deploy AI for clinical workflows and accelerated medical imaging research.


The Clara Platform's Compute Foundation is based on CUDA acceleration and system software libraries for computation and visualisation that make the capabilities of GPUs accessible via SDKs and low-level APIs. The Clara platform's Compute Foundation is based on CUDA acceleration and system software libraries for computation and visualisation that make the capabilities of GPUs available via SDKs and low-level APIs.


  • More cone-beam CT research is done with CUDA than with any other.
  • Accelerator technology CUDA outperforms other accelerator technologies by an order of magnitude or more.
  • The latest algorithmic developments being performed are all CUDA-accelerated.


Download the latest advanced visualisation libraries:


CLARA GENOMICS is designed to address the growing scale and complexity of genomics sequencing and analysis with accelerated and intelligent computing.

The Clara Genomics Analysis SDK is now available under open source terms to provide free and open access to developers; please click below to access the release via GitHub:

GPU-accelerated implementation of the Racon consensus module for de novo genome assembly.
This open source version adds the cudaAligner module for accelerated alignment, including Ukkonen's algorithm and Myers' bit algorithm

Download on GitHub
In genomics, there are several transformative trends that are putting computing at the forefront of advancements: increasing device throughput, AI-powered analytics applications, and reducing the cost of sequencing to study large populations. NVIDIA's GPU Accelerated Computing platform enables real-time genomics workflows with high-performance computing, deep learning and analytics on a single architecture that lives at the edge in the sequencer to the data centre and any public cloud.

A high-level workflow from sample preparation to final analysis that begins with the isolation of an organism's DNA. This isolated sample is then loaded onto a sequencing device where embedded GPUs are used to accelerate primary analysis and enable next-generation base calling with Deep Neural Networks (DNNs).

Secondary analysis or sequence analysis uses NVIDIA GPU computing for the Genome Analysis Toolkit (GATK), DNN-based variant calling and de novo genome assembly.

Our first release of the Clara Genomics SDK will focus on de novo assembly of long read sequencing from Oxford Nanopore and Pacific Biosciences, reducing analysis time from days to hours. The first version includes GPU-accelerated libraries and GPU-accelerated applications.


The Clara Genomics Technology Stack includes CUDA-accelerated software system libraries that form the basis for GPU computing.
  • CUDA Mapper - CUDA-based library sharing algorithms for overlapping sequencing reads.
  • CUDA Aligner - CUDA-accelerated library of sequencing read alignment algorithms used for genome assembly applications such as Racon and for variant calling.
  • CUDA POA - CUDA accelerated partial sequence alignment library used for genome assembly polishing with applications such as Racon.
These system libraries form the computational basis and enable GPU acceleration of the following applications:
  • Racon Polisher - An extension of the open source Racon Consensus genome assembly module that uses cudaPoa for accelerated partial order alignment.
  • Racon Aligner and Mapper - Will be available in upcoming versions.
Racon Project @ GitHub


CLARA EDUCATION SDK empowers data scientists and medical researchers with cutting-edge tools and technologies that accelerate data annotation, customisation and AI model development for medical imaging workflows.
Key features of the Clara Training SDK include:
  • APIs to add AI-powered annotation to any medical viewer with new features such as auto-annotation and interactive annotation modes, Annotation Server that provides pre-trained models to the client application, and client APIs hosted on Github that allow seamless integration with your medical viewer application.These capabilities are already built into the latest MITK Workbench plugin.
  • The SDK provides the ability to use techniques such as transfer learning to customise or train deep learning models from scratch, allowing Data Scientists to bring in their own model architectures and run Workflows This is made possible by a unified foundation of the Medical Model Archive (MMAR).
  • MMAR (Medical Model Archive) provides a model development environment; it defines a standard structure for storing and organising all artefacts created during the model development lifecycle.
  • MMAR contains NVIDIA pre-trained models based on AH-Net, DenseNet, ResNet, Dextr3D, packaged as complete 2D/3D model applications for organ-based segmentation, classification and annotation.


  • Bring your own models, transformations, readers, losses and metrics.
  • Configurable framework to facilitate deep learning tasks from medical images.
  • Medical Model Archive (MMAR) with deep learning models and artefacts.
  • Model adaptation and retraining that can be easily used in heterogeneous environments with multiple GPUs.
  • Model export API for easier deployment of applications for TensorRT-based inference.
"We were able to get our hands on NVIDIA's AI-powered annotation technology and integrate it into our viewer within a few days. We currently annotate many images - sometimes in the order of 1,000 or more per day. Any technology that can automate this process could have a significant impact on reducing the time and cost of annotations. We look forward to leveraging AI-powered workflows and collaborating
​​​​​​​with NVIDIA to solve these critical medical imaging problems."
- Mark Michalski, Executive Director am MGH & BWH Center for Clinical Data Science


Clara Deploy SDK provides a container-based development and deployment framework for building AI-accelerated medical imaging workflows. It uses Kubernetes under the bonnet and allows developers and data scientists to define a multi-tier container-based pipeline. The modular architecture allows developers to use the platform's offerings at the end or customise the workflow pipelines with their own algorithms.
The functions that make up the Clara Deploy SDK include:
  • Data ingestion interface for communication with the hospital's PACs system.
  • Core services to orchestrate and manage resources for workflow deployment and development.
  • Reference AI applications that can be used as-is with custom data or modified with custom AI algorithms.
  • Finally, the Clara Deploy framework also includes visualisation capabilities to monitor progress and display final results.