NVIDIA Telecommunications Vietnam

Vietnam Post and Telecommunications Group (VNPT)

Real-time Analytics

VNPT needed a high-performance traffic-monitoring system that could analyze thousands of camera feeds and hundreds of thousands of AI service requests to improve congestion management and road safety at busy intersections.

Reduced training time from weeks to days
hundreds of thousands of AI service requests
thousands of cameras

Reported impact

Business outcome

VNPT reduced model-training time from weeks to days, improved traffic-flow visibility, and built a safer road-monitoring system that can help authorities respond to incidents more quickly and efficiently.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

VNPT AI built a traffic-monitoring platform on NVIDIA AI Enterprise so it could analyze thousands of camera feeds and handle hundreds of thousands of AI service requests across busy Vietnamese intersections. Video streams are ingested through NVIDIA Metropolis and the NVIDIA DeepStream SDK, where pretrained models, TensorRT optimization, and containerized AI Enterprise components detect vehicles and pedestrians, interpret traffic signals, and classify potential hazards. Training runs on NVIDIA DGX A100 systems, while inference is split between NVIDIA Jetson devices at the edge and on-premises servers equipped with NVIDIA T4 and A30 GPUs to balance low-latency roadside processing with centralized multi-stream analytics. The result is a hybrid edge-plus-datacenter architecture that feeds real-time congestion and safety insights to response teams and gives VNPT a reusable base for future AI services.

Data flow

Traffic-camera feeds are processed with Metropolis and DeepStream, edge systems with Jetson interpret signals and track road users, backend T4 and A30 servers aggregate multiple streams, and the resulting alerts and congestion insights are delivered to authorities and response teams.

Solution architecture

8 components · 4 layers
  1. Ingest
    • NVIDIA Metropolis Supplies the end-to-end video analytics framework VNPT uses to process traffic-camera feeds.
  2. Compute
    • NVIDIA DGX A100 Handles AI model training for VNPT's traffic-monitoring workloads.
    • NVIDIA T4 Runs backend inference workloads for concurrent multi-stream video processing.
    • NVIDIA A30 Runs backend inference workloads for concurrent multi-stream video processing.
  3. Serving
    • NVIDIA DeepStream SDK Runs the real-time video analytics pipeline that detects and classifies road users and events.
    • NVIDIA TensorRT Optimizes VNPT's vision models for higher-performance inference.
    • NVIDIA Jetson Supports edge deployment where roadside systems interpret signals and track objects near the cameras.
  4. Orchestration
    • NVIDIA AI Enterprise Provides the containerized software layer, pretrained models, frameworks, and support VNPT uses to build and deploy the traffic solution.

Architecture clues

  • The deployment uses both edge Jetson systems and on-premises T4 and A30 GPU servers
  • The models detect cars, motorcycles, bicycles, and pedestrians and interpret traffic signals
  • VNPT used NVIDIA Enterprise Support to resolve performance issues and optimize infrastructure

Evidence from the source

Reduced training time from weeks to days
The video analytics applications are deployed at the edge with NVIDIA Jetson or on premises with T4 and A30 GPUs.
VNPT used NVIDIA Metropolis to process and analyze multiple video feeds from strategically placed cameras around some of the most traffic-heavy intersections in major cities.
they needed extremely accurate, high-performance AI models to handle hundreds of thousands of AI service requests on thousands of cameras across Vietnam.
Transparency Evidence & methodology 75/100 reference value · 87% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 specificity8/10
    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

    Vietnam Post and Telecommunications Group (VNPT) customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/vnpt-advances-traffic-safety-with-ai-enterprise/
    Source type
    Vendor-published customer story
    Published
    Aug 25, 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 Aug 25, 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.