NVIDIA Media and Entertainment Global

Skyrim.AI

Skyrim.AI needed to deliver real-time, broadcast-quality spatial media for sports, but volumetric capture from dozens of cameras created interoperability, reconstruction, networking, and data-throughput challenges that traditional broadcast stacks could not handle efficiently.

120 frames per second
More than two dozen 8K cameras
Over a terabit of data moved through the pipeline
Reduced development time by a full year

Reported impact

Business outcome

Skyrim.AI cut development time by a full year, lowered infrastructure costs by replacing specialized hardware with software-defined workflows, and created a platform that supports new revenue streams, richer content production, and personalized sports experiences for global audiences.

  • Cost Reduction
  • Customer Experience
  • Revenue Growth
  • Speed to Market

Solution

Skyrim.AI built its Atlas platform as a software-defined spatial media stack on NVIDIA networking, capture, and inference technologies. Live camera data is sent directly into GPUs with GPUDirect and NVIDIA Rivermax using SMPTE 2110 low-latency transport and PTP synchronization, while NVIDIA Quantum InfiniBand, NVLink, and GPU clustering scale 3D reconstruction across distributed systems. For AI services, the company uses NIM microservices, Maxine NIM, and Instant NeRF-style workflows together with proprietary radiance-field models to reconstruct and render spatial scenes in real time. RTX 6000 Ada GPUs handle camera-to-GPU capture and current inference, while L40S and H100-class GPUs support rendering and future large-scale training. This architecture allows Atlas to deliver broadcast and in-venue experiences from the same spatial capture pipeline while keeping the platform adaptable for OTT, fantasy sports, and interactive fan experiences.

Data flow

More than two dozen 8K cameras stream synchronized data through Rivermax and GPUDirect into GPUs, reconstruction and radiance-field pipelines process the volumetric data across NVLink and InfiniBand-connected clusters, and Atlas delivers broadcast, venue, and interactive spatial outputs to downstream media experiences.

Solution architecture

9 components · 3 layers
  1. Ingest
    • NVIDIA Rivermax Optimizes the low-latency capture pipeline from synchronized cameras into GPU processing.
    • GPUDirect Storage Moves camera data into GPU processing paths without CPU bottlenecks.
  2. Compute
    • NVIDIA Quantum InfiniBand Networking Transfers 3D data across GPU clusters for large-scale, distributed reconstruction.
    • NVLink Supplies high-bandwidth interconnect between GPUs inside the reconstruction stack.
    • NVIDIA RTX 6000 Ada Generation GPUs Handle camera-to-GPU capture and current inference workloads for the platform.
    • NVIDIA L40S Supports 3D rendering and planned future NIM inference workloads.
    • NVIDIA H100 Provides the higher-end GPU tier Skyrim.AI uses or plans to use for large-model training.
  3. Serving
    • Atlas platform Acts as Skyrim.AI's software-defined spatial media stack for capture, reconstruction, and delivery.
    • NIM Provides interoperable AI microservices that help power inference across the spatial media pipeline.

Architecture clues

  • Different GPU tiers are assigned to capture, rendering, inference, and future training tasks.
  • Skyrim.AI uses a software-defined spatial media stack rather than hardware-bound broadcast workflows.
  • The capture path relies on PTP sync and SMPTE 2110 to keep multi-camera data tightly aligned.

Evidence from the source

By leveraging standardized solutions like Rivermax, development time has been significantly reduced by a full year.
Delivering real-time, broadcast-quality volumetric video involves moving over a terabit of data from more than two dozen 8K cameras at 120 frames per second via SMPTE 2110 directly into GPUs with GPU Direct.
NVIDIA's AI and GPU technologies, including NVIDIA NIM™ microservices, NVIDIA Quantum InfiniBand networking, and NVIDIA NVLink™, have enabled Skyrim.AI to build a scalable, high-performance infrastructure with real-time, broadcast-quality spatial media integration.
NVIDIA® GPUDirect® enables efficient data transfer from cameras to GPUs, bypassing the CPU.
NVIDIA® Rivermax® to enhance the capture pipeline, significantly reducing development time and costs.
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
    5 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

    Skyrim.AI customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/skyrim-ai-sports-media-spatial-experiences/
    Source type
    Vendor-published customer story
    Published
    Apr 30, 2025
    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 Apr 30, 2025 · 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.