Analyzing 100k genomes dragged at 8 hours per case. GPU nodes cut processing to 40 minutes, unlocking the program’s scale.
A genomic medicine program within a major healthcare system aims to sequence 100,000 patient genomes and link them to full electronic health records for research and clinical use.
Standard genomic analysis on high-performance computing clusters faced severe bottlenecks, with variant calling taking up to eight hours per genome...
“Our goal is to enable genomic research in the medical system in a way that links the data to the full electronic health records of a patient. To link the entire depth, history, and every feature that exists in electronic records that often can’t really be obtained from biobanks.”
Academic medical center for patient care, medical education, and biomedical research.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Related implementations across industries and use cases
Legacy CPUs bottlenecked genome analysis at 50 hours per sample. GPU pipelines cut this to 31 minutes—97x faster.
CPU bottlenecks delayed urgent post-surgery biopsies. GPU-accelerated pipelines now empower lab teams to meet strict delivery targets.
Clinicians spent 50 hours per case sifting data. AI now ranks disease risks, cutting diagnosis time from 7 years to 1 month.
Legacy CPUs bottlenecked genome analysis at 50 hours per sample. GPU pipelines cut this to 31 minutes—97x faster.
CPU bottlenecks delayed urgent post-surgery biopsies. GPU-accelerated pipelines now empower lab teams to meet strict delivery targets.
Clinicians spent evenings buried in manual notes. AI now generates charts from audio, scaling from primary care to oncology.
Clinicians struggled to stitch together charts. Claude now delivers instant, cited summaries, cutting latency by 61%.
Analyzing 10TB of weekly telemetry took IT specialists days. Now, engineers ask AI in natural language to instantly retrieve charts.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.
Analyzing 100k genomes dragged at 8 hours per case. GPU nodes cut processing to 40 minutes, unlocking the program’s scale.
A genomic medicine program within a major healthcare system aims to sequence 100,000 patient genomes and link them to full electronic health records for research and clinical use.
Standard genomic analysis on high-performance computing clusters faced severe bottlenecks, with variant calling taking up to eight hours per genome...
“Our goal is to enable genomic research in the medical system in a way that links the data to the full electronic health records of a patient. To link the entire depth, history, and every feature that exists in electronic records that often can’t really be obtained from biobanks.”
Academic medical center for patient care, medical education, and biomedical research.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Related implementations across industries and use cases
Legacy CPUs bottlenecked genome analysis at 50 hours per sample. GPU pipelines cut this to 31 minutes—97x faster.
CPU bottlenecks delayed urgent post-surgery biopsies. GPU-accelerated pipelines now empower lab teams to meet strict delivery targets.
Clinicians spent 50 hours per case sifting data. AI now ranks disease risks, cutting diagnosis time from 7 years to 1 month.
Legacy CPUs bottlenecked genome analysis at 50 hours per sample. GPU pipelines cut this to 31 minutes—97x faster.
CPU bottlenecks delayed urgent post-surgery biopsies. GPU-accelerated pipelines now empower lab teams to meet strict delivery targets.
Clinicians spent evenings buried in manual notes. AI now generates charts from audio, scaling from primary care to oncology.
Clinicians struggled to stitch together charts. Claude now delivers instant, cited summaries, cutting latency by 61%.
Analyzing 10TB of weekly telemetry took IT specialists days. Now, engineers ask AI in natural language to instantly retrieve charts.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.