AI Compute & Silicon · nvidia.com
NVIDIA
141 verified customer use cases (96% with quantified ROI · Avg Value Score 93.3 · 100% with mapped architecture components).
- Verified stories
- 141
- Quantified ROI
- 96%
- Avg value score
- 93.3
- Industries served
- 17
- Distinct products
- 428
NVIDIA Sample Apr 8, 2026 SimBioSys Traditional breast-cancer presurgical planning relied on grayscale MRI images and short radiology summaries, which made tumor geometry hard to interpret, contributed to variable surgical decisions and unnecessary… above 20 percent MONAI · NVIDIA A100 Tensor Core GPUs · NVIDIA CUDA-X NVIDIA Sample Apr 2, 2026 Topaz Labs Topaz Labs had to ship and maintain large sets of static inference engines across millions of PCs and many RTX GPU generations, which made releases heavy, slowed updates, and added weeks of precompilation work whenever… 1,620 frames of video from 1080p to 4K NVIDIA GeForce RTX 5090 GPU · NVIDIA RTX Systems · NVIDIA TensorRT for RTX NVIDIA Sample Mar 31, 2026 The Ohio State University The Ohio State University needed enough compute and data-engineering capacity to assemble a global biodiversity dataset and train a foundation model that could identify species across nearly a million taxa, which was… 1.36 million noisy taxonomic hierarchies NVIDIA A100 GPUs · NVIDIA H100 GPUs · TaxonoPy NVIDIA Sample Est. · Mar 19, 2026 Merck Lead optimization in small-molecule research takes months, and most drug candidates never make it to clinical testing, so Merck needed a way to predict molecular behavior earlier and reduce wasted lab work. 30% or more Clara Open Models · KERMT · NVIDIA BioNeMo NVIDIA Sample Mar 17, 2026 Mastercard Mastercard wants to turn its transaction data into a reusable AI insights engine for payments and commerce, especially to strengthen cybersecurity and fraud detection, improve personalization and loyalty use cases, and… billions of transactions Databricks · Nvidia's advanced accelerated computing platform · large tabular model (LTM) NVIDIA Sample Mar 10, 2026 Terray Therapeutics Terray Therapeutics needed to search an almost infinite chemical space with generative models, but multi-node training, terabyte-scale datasets, and iterative hyperparameter experiments were too cumbersome on a mix of… 32-GPU cluster COATI · NVIDIA Base Command Platform · NVIDIA DGX Cloud NVIDIA Sample Mar 5, 2026 WEIDER Probiotics WEIDER Probiotics needed to provide instant, personalized product guidance without the wait times, staffing overhead, or impersonal interactions that came with traditional customer-service teams and basic chatbots. Each NVIDIA L4 GPU supports multiple simultaneous users Custom Mandarin language models · NVIDIA ACE 5.0 · NVIDIA L4 GPUs NVIDIA Sample Mar 4, 2026 Israel Meteorological Service The Israel Meteorological Service needed to produce faster and more localized weather forecasts for floods, fire seasons, and other severe conditions, but conventional numerical weather prediction required large CPU… 2.5-kilometer resolution forecasts NVIDIA Earth-2 · NVIDIA Earth-2 Corrector Diffusion (CorrDiff) · NVIDIA PhysicsNeMo NVIDIA Sample Archived · Feb 9, 2026 Clinomic Clinomic needed to consolidate ICU patient data, reduce documentation burden, and run bedside analytics and telemedicine functions without relying on cloud connectivity that could introduce latency or operational risk… 143% increase in patient-care capacity NVIDIA Jetson AGX Xavier · NVIDIA TensorRT NVIDIA Sample Feb 5, 2026 EDAG Production Solutions EDAG needed to help manufacturers launch and optimize factories faster, but fragmented planning tools, disconnected supplier data, and manual status tracking slowed collaboration, increased errors, and delayed readiness… More than 100 workers trained virtually before installation EDAG digital twin platform · NVIDIA Omniverse · OpenUSD NVIDIA Sample Feb 5, 2026 Lightwheel Lightwheel needed a way to overcome slow real-world robotics data collection and the sim-to-real gap so customers could train and deploy embodied AI systems without months of physical prototyping and expensive on-robot… 100 to 1000 times Isaac GR00T N1.5 · Isaac Lab · NVIDIA Isaac Sim NVIDIA Sample Feb 4, 2026 VinuesaLab at the University of Michigan VinuesaLab needed high-fidelity, city-scale urban airflow simulation for planning and resilience work, but traditional CFD workflows were too slow, too resource intensive, and largely constrained to 2D approximations. 10x Faster Training Diff-SPORT · NVIDIA A100 Tensor Core GPUs · NVIDIA PhysicsNeMo NVIDIA Sample Feb 4, 2026 MITRE MITRE needed a secure, shared AI environment that government sponsors and researchers could use to test advanced models across domains without duplicating infrastructure or weakening controls around sensitive data. 1-kilometer level precision NVIDIA DGX SuperPOD · NVIDIA Earth-2 · NVIDIA Omniverse NVIDIA Sample Feb 4, 2026 Foxconn Foxconn needed a faster way to design, replicate, and optimize complex global factory operations because traditional facility planning, robotics programming, and environmental analysis were too slow, siloed, and… 150x faster computational fluid dynamics (CFD) simulations FoundationPose · NVIDIA Isaac Lab · NVIDIA Isaac Sim NVIDIA Sample Feb 2, 2026 Prima Mente Prima Mente needed to model genome-scale epigenetic and DNA signals for neuroscience and precision diagnostics, but whole-genome methylation data, clinical benchmark sets, and foundation-model training all exceeded what… 0.89 accuracy, rising to 0.97 with protein tests NVIDIA DGX Cloud · NVIDIA Quantum InfiniBand Networking · Nebius AI Cloud NVIDIA Sample Feb 2, 2026 NVIDIA NVIDIA needed to move employee-built chatbots and agents out of shadow-AI pilots and onto a unified, secure, high-performance platform that could support hundreds of governed AI workflows across engineering, software… from 15 hours to one hour NVIDIA AI Blueprint for Retrieval-Augmented Generation (RAG) · NVIDIA AI Enterprise · NVIDIA GPU Operator NVIDIA Sample Feb 2, 2026 Workr Labs Workr needed to make robot automation practical for high-mix, low-volume manufacturers where each new part or cell layout traditionally required weeks of expert programming, calibration, and fixture design. 2x NVIDIA RTX PRO 6000 Blackwell Max-Q's attached to any robot cell Detectron2 · NVIDIA Isaac FoundationPose · NVIDIA Isaac cuMotion NVIDIA Sample Feb 2, 2026 Leidos Leidos needed to reduce information overload and improve coordination in fast-moving disaster scenarios where human commanders must ingest video, radio, sensor, and public-safety signals quickly enough to allocate… NVIDIA AI Enterprise · NVIDIA Metropolis · NVIDIA NIM NVIDIA Sample Feb 2, 2026 DeepL DeepL needed to scale high-quality, context-aware translation for millions of daily users and hundreds of thousands of business customers without sacrificing latency, accuracy, or the ability to train larger models for… 10x faster translation processing Arion · DGX GB200 systems · NVIDIA DGX SuperPOD NVIDIA Sample Feb 2, 2026 MediaTek MediaTek needed a scalable, secure AI factory that could sustain thousands of monthly training iterations, billions of inference tokens, and proprietary R&D workloads such as code completion and documentation without… 40% improvement in inference speed NVIDIA Blackwell · NVIDIA DGX Spark · NVIDIA DGX SuperPOD NVIDIA Sample Feb 2, 2026 UNSW Sydney UNSW Sydney and the NSW Telestroke Service needed fast, quantitative stroke imaging for regional hospitals, but manual hemorrhage and infarct segmentation took too long and made it hard to guide treatment decisions in… 20 minutes to seconds Auto3DSeg · MONAI Label · NVIDIA GPUs on AWS NVIDIA Sample Feb 2, 2026 RBC Capital Markets RBC Capital Markets needed to speed up document-heavy research and banking workflows so analysts and bankers could deliver faster insights, prepare client materials more efficiently, and answer complex questions without… 10x increase in document processing capacity NVIDIA AI Enterprise · NVIDIA NIM · NVIDIA NeMo NVIDIA Sample Feb 2, 2026 Skild AI Skild AI needed a way to train robots that can adapt across many bodies, tasks, and environments, but collecting enough real-world robotics data was too slow and expensive to produce robust general-purpose physical AI. $4,000-$15,000 systems versus $250,000+ traditional systems NVIDIA Cosmos Transfer · NVIDIA Isaac Lab · NVIDIA Omniverse NVIDIA Sample Feb 2, 2026 Fisia Group Fisia needed to improve fit confidence, conversion, and basket size for Nike shoppers while reducing returns and making the same personalized experience work consistently in stores, on mobile devices, and online. 128% ROI Doris.ai · NVIDIA A10 Tensor Core GPUs · NVIDIA TensorRT