Bring the model to the patient data.
On-premises GPU systems for medical imaging AI, clinical language models, genomics and drug discovery.
The constraint
Health data is among the most sensitive personal data under India's DPDP Act, and hospital governance often keeps imaging studies and records on site. Pharma and biotech teams face a version of the same problem: compound libraries and trial data are the most valuable data they own. Both need serious GPU capacity that stays under their own control.
Workloads
Medical imaging AI
Train and run segmentation and detection models on CT, MRI and digital pathology images, using frameworks such as MONAI, on data that never leaves the site.
Clinical language models
Serve private LLMs that summarise notes, draft letters and search records, with every prompt kept on the hospital network.
Genomics
Run GPU-accelerated sequencing pipelines such as NVIDIA Parabricks to turn sequencing runs into variant calls faster.
Molecular dynamics & structural biology
Run long simulations and structure-prediction models for drug discovery.
Systems
- GLabs GPU Servers — training, inference and simulation nodes.
- GLabs Vault — imaging archives, genomic data and model versions.
- GLabs AI Workstations — research and annotation seats.
Deployed with
GLabs AI Stack for imaging, clinical and structure-prediction models. GLIDS for genomics and simulation pipelines.
Related solutionsMolecular Dynamics & Structural BiologyFine-Tuning & Domain Adaptation
FAQs
Can AI run on patient data without it leaving the hospital?
Yes. Models are trained and served on GPU servers inside your network, so images, records and prompts stay on site. Access and audit follow your own hospital controls.
Are your systems medical devices?
No. Our systems are compute infrastructure. Software that informs diagnosis may be regulated as a medical device under India's Medical Devices Rules, 2017, and that approval belongs to the software and its maker, not the hardware it runs on.
What hardware do we need for digital pathology?
Whole-slide images are gigapixel files, so storage throughput matters as much as GPU power. We size storage, network and GPUs together to your slide volume.
How do you size a genomics system?
From samples per day, coverage and the pipeline you run. GPU-accelerated pipelines process germline and somatic analysis on multi-GPU servers, and storage is sized for raw reads as well as results.
Keep the data where it is. We'll bring the compute.
All product and company names mentioned are trademarks of their respective owners.