NVIDIA Nonprofit United States

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
190 nations
300-fold performance increase
thousands of researchers

Reported impact

Business outcome

MITRE increased AI-compute performance by 300x, created a shared experimentation environment for thousands of researchers, advanced 1-kilometer weather forecasting over water, and accelerated work on cybersecurity and public-benefits AI use cases.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

MITRE consolidated scattered accelerated-computing resources into the Federal AI Sandbox, a DGX SuperPOD-powered environment built to let U.S. agencies and researchers safely experiment with AI across cybersecurity, weather, benefits, and other public-interest workloads. The platform can deploy the latest open-source models while maintaining the controls needed for sensitive and classified information, giving thousands of internal researchers a shared environment instead of isolated projects. MITRE uses the same stack with NVIDIA Earth-2, NVIDIA Omniverse, and NVIDIA PhysicsNeMo to train and visualize high-resolution weather models, and it pairs DGX SuperPOD training with edge systems and simulation in a three-computer architecture for ocean and autonomy work. By centralizing multimodal AI training and inference, the sandbox supports everything from satellite imagery analysis to cybersecurity foundation models and public-benefits automation.

Data flow

Government and MITRE datasets are brought into the sandbox for centralized model training on DGX SuperPOD systems, specialized workflows use Earth-2, PhysicsNeMo, and Omniverse for simulation and visualization, and resulting models are shared back to researchers and agency programs for operational use.

Solution architecture

4 components · 2 layers
  1. Compute
    • NVIDIA DGX SuperPOD Provides the centralized AI infrastructure for the Federal AI Sandbox.
    • NVIDIA Earth-2 Supports MITRE's weather-simulation and forecasting research inside the sandbox.
    • NVIDIA PhysicsNeMo Helps MITRE experiment with advanced physical-modeling and forecasting workflows.
  2. Serving
    • NVIDIA Omniverse Powers custom visualization and simulation workflows tied to weather and ocean research.

Architecture clues

  • MITRE uses a three-computer architecture that combines edge AI, DGX SuperPOD training, and Omniverse simulation
  • The platform consolidates multiple generations of NVIDIA accelerated computing into one sandbox
  • The sandbox is designed to handle controlled and classified information while exposing open-source models

Evidence from the source

Enhancing threat analysis across 190 nations with a cybersecurity foundational model
MITRE established the Federal AI Sandbox, powered by NVIDIA DGX SuperPOD, to improve AI access and accelerate innovation across government agencies.
The addition of the DGX SuperPOD provides a 300-fold performance increase over their previous AI computer capabilities.
This initiative promises significant advancements in maritime safety, shipping efficiency, and extreme weather preparedness.
Transparency Evidence & methodology 85/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. NVIDIA

    MITRE customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/mitre-federal-ai-sandbox-unleashes-ai-potential-for-public-good/
    Source type
    Vendor-published customer story
    Published
    Feb 4, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Feb 4, 2026 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.