NVIDIA Media and Entertainment United States

FanDuel TV and Verizon

Real-time Analytics

FanDuel TV and Verizon needed a portable production architecture that could move high-volume live sports feeds with lower latency and stronger security than cloud backhaul while still supporting AI-assisted camera selection and on-site editorial control.

Hardware footprint condensed from multiple racks to one portable unit
More than 130 racetracks nationwide
More than 70,000 live horse races annually
Over 50% lower latency versus traditional cloud workflows

Reported impact

Business outcome

The pilot cut latency by more than 50 percent versus traditional cloud workflows, gave FanDuel immediate editorial control near the source, reduced hardware from multiple racks to one portable unit, and created a scalable path for AI-assisted production across more than 130 racetracks.

  • Customer Experience
  • Scalability
  • Speed to Market

Solution

Verizon built its Private 5G Network with Enterprise AI for FanDuel TV as an on-site live-media stack that combines wireless connectivity, edge compute, and AI inference in one portable unit. Camera feeds are ingested over Verizon's licensed-spectrum private 5G network and processed at the edge with NVIDIA Holoscan for Media running on bare-metal infrastructure with NVIDIA L40S GPUs. NVIDIA AI Enterprise and NIM microservices support the AI runtime, which can apply object detection, feed prioritization, captioning, and other media workflows without sending traffic back to a remote cloud. The platform is SMPTE ST 2110 compliant and includes timing, control, and container orchestration so broadcasters can train and deploy custom LLM and VLM workflows on site while keeping sensitive live-production data local.

Data flow

Live camera feeds are transmitted over Verizon's private 5G network into an edge compute module, where Holoscan for Media and NIM-backed services run on L40S GPUs. The AI layer analyzes video in real time to prioritize feeds, identify key race moments, and return low-latency outputs to producers so editorial control stays on site rather than in a distant cloud.

Solution architecture

5 components · 3 layers
  1. Compute
    • NVIDIA L40S GPUs Supply the high-performance on-site compute used for real-time media inference and custom AI model deployment.
  2. Serving
    • NVIDIA Holoscan for Media Processes video and audio at the edge so AI-assisted production logic can run near the source.
    • NVIDIA NIM Offers reusable microservices inside the edge stack for deploying AI-assisted media workflows.
  3. Orchestration
    • Verizon Private 5G Network with Enterprise AI Provides the portable wireless and edge-compute environment used to transport and manage live production traffic on site.
    • NVIDIA AI Enterprise Provides the software stack used to run enterprise AI workloads in the live-media edge environment.

Architecture clues

  • Partner demo used Mistral 12B for object detection and feed prioritization
  • Supports on-site training and deployment of custom LLM and VLM workflows
  • The edge stack is fully IP-based and compliant with SMPTE ST 2110
  • Uses licensed spectrum with mmWave, C-band, and CBRS radios

Evidence from the source

Built on NVIDIA Holoscan for Media, it enables real-time AI inference for video and audio and supports multi-vendor workflows via container orchestration.
FanDuel's pilot using Verizon's Private 5G Network with Enterprise AI running on NVIDIA AI Enterprise and Holoscan for Media achieved over 50% lower latency and enabled immediate editorial control near the source for live race production.
It runs on bare-metal infrastructure leveraging NVIDIA L40S GPUs for high-performance compute, networking, and storage.
This pilot shows how Private 5G and edge AI can transform high-volume live sports production.
Transparency Evidence & methodology 90/100 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 value90/100
  • 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 scale10/10
    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

    FanDuel TV and Verizon customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/verizon-private-5g-network-with-enterprise-ai-for-live-media/
    Source type
    Vendor-published customer story
    Published
    Feb 2, 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 2, 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.