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
Architecture Stack Signature
6-Layer Production Component Breakdown
NVIDIA NeMo 12 NVIDIA DGX SuperPOD 10 NVIDIA H100 GPUs 10
NVIDIA NIM 19 NVIDIA Triton Inference Server 15 NVIDIA Omniverse 13
NVIDIA AI Enterprise 20 OpenUSD 6 NVIDIA Base Command Platform 4
NVIDIA AI Enterprise 9 NVIDIA NeMo Guardrails 4 AI Configurator 1
NVIDIA NeMo Curator 5 10x Genomics Xenium Analyzer 1 AVAI One Sensor 1
DDN high-performance storage 1 Microsoft Azure 1 Milvus 1
Product & Model Ecosystem
Most Deployed Products & Co-occurring Pairs
Top named products & AI models
NVIDIA AI Enterprise 29 NVIDIA NIM 19 NVIDIA Omniverse 17 NVIDIA NeMo 16 NVIDIA Triton Inference Server 15 NVIDIA TensorRT 13 NVIDIA DGX SuperPOD 11 NVIDIA CUDA 10
Most frequent co-deployed product pairs
- NVIDIA AI Enterprise + NVIDIA NIM 12 stories
- NVIDIA NIM + NVIDIA NeMo 8 stories
- NVIDIA AI Enterprise + NVIDIA NeMo Retriever 6 stories
- NVIDIA AI Enterprise + NVIDIA Triton Inference Server 6 stories
- NVIDIA NeMo + NVIDIA Riva 6 stories
Adoption Footprint
Sector, Region & Organization Scale
Top adopting industries
Geographic footprint
- North America 56 (40%)
- Global / Multi-region 43 (31%)
- Asia-Pacific 18 (13%)
- Europe 14 (10%)
Organization scale
- Enterprise 70 (50%)
- SMB 37 (26%)
- Public sector 22 (16%)
- Mid-market 7 (5%)
Verified Business Impact
Outcome Categories & Customer ROI Proof
Primary outcome drivers
NVIDIA Sample Oct 9, 2026 California Department of Transportation (Caltrans) Caltrans needed to inspect and monitor roughly 1,200 active transportation projects more frequently and safely, but manual site visits were slow, inconsistent, and risky for staff working around traffic, bridges, and… Remote operations up to 100 miles Esri ArcGIS · NVIDIA CUDA · NVIDIA Jetson Orin NVIDIA Sample Oct 6, 2026 University of Pennsylvania Penn needed shared AI infrastructure that could support researchers across many disciplines without weeks-long wait times, fragmented storage, or the cost barriers that kept frontier-scale training and inference off… 1.5 years of compute time reduced to three days for a 40,000-document project NVIDIA DGX B200 systems · NVIDIA DGX SuperPOD · NVIDIA InfiniBand NVIDIA Sample Sep 30, 2026 Corteva Corteva needed to evaluate billions of host-pathogen protein interactions and massive metabolomics datasets faster, but early-stage agricultural discovery pipelines were too computationally expensive to prioritize… 15-fold BioNeMo Boltz-2 NIM · NVIDIA H200 GPUs · NVIDIA cuML NVIDIA Sample Sep 30, 2026 EPRI EPRI needed to run advanced AI for sensitive utility documents and weather-planning workflows locally, because energy-sector research data could not leave controlled environments and shared compute delays limited how… 150 tokens/sec aggregate across 10 concurrent Power Chat users DGX Spark · Dell Pro Max with GB10 · NVIDIA CBottle NVIDIA Sample Sep 22, 2026 DELFI Diagnostics DELFI Diagnostics needed to speed genomic analysis for cancer screening and therapeutic monitoring, but CPU-bound alignment and somatic variant-calling steps created turnaround-time and cost bottlenecks that limited how… 10–20 business days DeepSomatic · MutectCaller · NVIDIA Parabricks NVIDIA Sample Sep 22, 2026 Sluicebox Sluicebox needed to turn fragmented bills of materials, supplier records, PDFs, CAD files, and emissions inventories into timely product carbon assessments, but manual life-cycle-analysis work was slow, expensive, and… 51-80% lower inference costs Lucy · NVIDIA Nemotron 3 Ultra · carbon graph NVIDIA Sample Sep 15, 2026 Lambda Lambda needed to raise token throughput and improve AI-cloud economics, but fixed rack and facility power budgets were leaving GPU capacity stranded and limiting how many nodes could run safely at once. +17% Hybrid Training Throughput Dynamic Power Software · NVIDIA DSX AI Factory Platform · NVIDIA DSX MaxLPS NVIDIA Sample Sep 14, 2026 Pinterest Pinterest needed to serve vision-language AI for shopping and discovery at internet scale, but multimodal requests involving thousands of images created prefill-heavy inference, memory pressure, and routing challenges… 14,000 NVIDIA GPUs AI Configurator · Dynamo Planner · NVIDIA Blackwell B200 GPUs NVIDIA Sample Sep 4, 2026 Aigen Aigen needed to train field-robot perception and control systems for highly variable agricultural environments, but collecting and labeling enough real-world crop and weed data was too slow, seasonal, and expensive to… 99% synthetic data and 1% real-world data Alchemy · Cosmos Transfer 2.5 · NVIDIA Cosmos 3 NVIDIA Sample Sep 1, 2026 Harry Reid International Airport Harry Reid International Airport needed real-time visibility into queues and passenger flows, but a legacy lidar system and manual phone-driven monitoring could not provide actionable analytics across two terminals, 110… 500 existing security cameras NVIDIA CUDA · NVIDIA Dynamo · NVIDIA Metropolis NVIDIA Sample Sep 1, 2026 Onetera Onetera needed to help more than 90 municipalities turn fragmented regulations, zoning maps, permit rules, and legacy documents into fast resident-facing services without imposing heavy IT overhead or expensive manual… 1.7x increase in data throughput GPT OSS 20B · Government Intelligence Agent · LangGraph NVIDIA Sample Aug 27, 2026 Texas A&M University Texas A&M needed to give researchers across seven institutions access to shared GPU infrastructure for AI and simulation, but isolated workstations, slow grant-funded clusters, and long queues were limiting… $1M saved on a single project DDN high-performance storage · NVIDIA Base Command Manager · NVIDIA DGX SuperPOD NVIDIA Sample Aug 25, 2026 Neuron Systems Neuron Systems needed an always-on AI sports newsroom that could analyze every play, sustain multi-agent reasoning during long live broadcasts, and generate multilingual commentary without the latency, rate limits, and… 1 million audited AI traces NVIDIA CUDA · NVIDIA H100 GPUs NVIDIA Sample Aug 25, 2026 PYLER PYLER needed to verify brand safety across millions of videos per day, but keyword and rule-based moderation could not provide the semantic precision, throughput, or operating cost profile required for real-time… 10X reduction in operational costs NVIDIA CUDA · NVIDIA DGX Platform · NVIDIA Mission Control NVIDIA Sample Aug 25, 2026 Siemens Siemens needed to help industrial teams overcome fragmented engineering tools, labor shortages, and slow design and maintenance workflows so they could design, simulate, and operate complex products and factories faster… 10%–15% reductions in capital expenditure NVIDIA CUDA · NVIDIA DGX B200 · NVIDIA DGX H200 NVIDIA Sample Aug 24, 2026 Noble Machines Noble Machines needed to train, evaluate, and deploy a general-purpose industrial robot fast enough for real customer environments without relying on slow, physical-only testing or cloud-dependent inference. 15 people NVIDIA GeForce RTX 5090 GPUs · NVIDIA H100 GPUs · NVIDIA Isaac GR00T 1.7 NVIDIA Sample Aug 14, 2026 Shopee Shopee needed large language models that could handle multilingual ecommerce tasks across Southeast Asia, Taiwan, and Brazil, but general-purpose frontier models degraded sharply on regional languages, shopping… 113x growth from 3 billion to 340 billion monthly tokens Compass v3.5 · NVIDIA A100 GPUs · NVIDIA H100 GPUs NVIDIA Sample Aug 12, 2026 Pegatron Pegatron needed to improve factory planning, quality control, and operational efficiency across large manufacturing sites, but manual line design, fragmented agent development, scarce defect data, and labor-intensive… 10% decrease in operational effort NVIDIA AI Enterprise · NVIDIA Cosmos · NVIDIA DGX NVIDIA Sample Aug 12, 2026 CYRAN AI Solutions CYRAN needed to deliver interactive geospatial intelligence in air-gapped and sovereign environments, but JPEG 2000 decode latency on large satellite scenes was the bottleneck that kept edge analytics from being… 26 GB satellite scene NVIDIA DGX Spark · NVIDIA nvJPEG2000 · PixelFlux NVIDIA Sample Aug 12, 2026 Internal Revenue Service IRS investigators needed to search multi-terabyte tax and transaction data to expose fraud and identity-theft rings, but CPU clusters and manual data stitching could not finish graph-heavy jobs quickly enough to support… 3+ terabyte dataset Cloudera · NVIDIA AI Enterprise · NVIDIA RAPIDS NVIDIA Sample Aug 5, 2026 Dassault Systèmes Dassault Systèmes needed to speed trusted simulation, move compliance checks earlier, and help aerospace and automotive customers make design decisions faster without sacrificing predictive accuracy or information… 10,000+ requirements 3DEXPERIENCE platform · NVIDIA CUDA-X · NVIDIA Nemotron open models NVIDIA Sample Aug 4, 2026 Trillion Labs Trillion Labs needed to build highly accurate Korean and multilingual LLMs, but the scarcity of high-quality Korean data and the CPU-heavy cost of language filtering, deduplication, and shuffling made large-scale data… 5% accuracy improvement for the Korean language using 100B tokens of curated data. Dask · NVIDIA H100 GPUs · NVIDIA NeMo Curator NVIDIA Sample Aug 3, 2026 StudyFetch StudyFetch needed to keep AI tutoring affordable while scaling live lecture transcription and personalized learning for millions of students, but off-the-shelf cloud transcription created cost and latency ceilings that… 100M+ learning interactions NVIDIA H100 GPUs · NVIDIA L40S GPUs · NVIDIA NIM NVIDIA Sample Jul 30, 2026 Stanford University Stanford needed a campus-wide GPU platform that could support interdisciplinary AI research at production scale without forcing labs to depend on fragmented clusters, complex allocations, or external resources for large… 18 million GPU hours NVIDIA Base Command Manager · NVIDIA DGX SuperPOD · NVIDIA Hopper GPUs