CHN New Material Technology Sdn Bhd helps organisations plan, deploy, and support AI-ready data centre infrastructure for smart city environments, enabling real-time analytics, secure edge workloads, and scalable digital services across transport, utilities, safety, and environmental operations.
Tier IV Uptime Standard
IoT Devices by 2030
Cooling Energy Reduction via AI
Smart City Market by 2030
CHN New Material Technology Sdn Bhd is a Malaysia-based technology company focused on AI data centre and smart city infrastructure. We support organisations that need dependable compute capacity, practical deployment guidance, and secure operations for data-intensive environments.
Infrastructure planning, system integration, deployment coordination, and technical advisory for AI-ready facilities and edge nodes.
Public agencies, developers, operators, researchers, and enterprises building data-driven services for cities and other critical environments.
We align design, delivery, and after-sales support around reliability, scalability, and site-specific operational requirements.
This section explains what CHN provides. The solutions section below shows where the infrastructure is applied across real operating environments.
Site planning, rack strategy, power and cooling alignment, and deployment preparation for AI workloads that need reliable high-density capacity.
Learn moreCoordination of servers, storage, networking, and platform components so new environments are ready for training, inference, and real-time data processing.
Learn moreIntegration approaches for central data centre capacity, remote edge nodes, sensor inputs, and operational systems that need low-latency data exchange.
Learn moreSupport for access control, monitoring, resilience planning, and ongoing technical assistance to help critical infrastructure remain stable over time.
Learn moreThe value is not just more computing power. It is faster decisions, lower waste, and infrastructure that can scale with city and enterprise demand.
Low-latency compute supports traffic control, incident review, and live operational dashboards so teams can act on data while it is still useful.
AI-assisted cooling, workload scheduling, and infrastructure visibility help reduce unnecessary power consumption and improve overall facility efficiency.
Modular deployment models let organisations expand capacity in phases instead of overbuilding upfront, reducing delivery risk and capital waste.
Well-designed AI infrastructure supports uptime targets, secure access control, and clearer data governance for public-facing digital services.
Our vision, mission, values, and philosophy form the foundation of everything we build and every partnership we cultivate.
To become the leading provider of AI data centre infrastructure for smart cities, enabling every urban centre to harness the power of artificial intelligence for a sustainable, efficient, and connected future.
To provide reliable, scalable, and energy-efficient GPU computing infrastructure that empowers cities, research institutions, and enterprises to solve their most complex computational challenges through AI.
We uphold uncompromising quality standards, maintain transparency in every engagement, and continuously push the boundaries of what AI infrastructure can achieve for urban communities.
We believe technology must serve human needs. Every solution we design centres on improving quality of life — shorter commutes, cleaner air, safer streets, and smarter resource management for citizens.
These are application areas where well-planned AI infrastructure can support better monitoring, faster decisions, and more efficient city operations.
Computer vision and live analytics help operators monitor congestion, coordinate signal timing, and improve incident response across major corridors.
Forecasting and control platforms support load balancing, demand visibility, and more responsive utility operations across power and resource networks.
AI-assisted monitoring and dispatch workflows help teams identify anomalies sooner and coordinate responses across departments with better situational awareness.
Sensor data pipelines support real-time visibility into air, water, and noise conditions, helping operations teams plan interventions faster and with better evidence.
These are the types of benchmarks many organisations evaluate when planning modern AI infrastructure for digital operations and smart city workloads.
Everything you need to know about how AI data centre infrastructure powers smart city operations, from technology fundamentals to real-world deployment considerations.
AI data centres are built for high-density, parallel computing workloads rather than standard office or web hosting needs. They typically require denser rack layouts, stronger cooling strategies, faster interconnects, and operating environments designed for continuous AI processing.
Public cloud introduces 50-200ms latency and ongoing data transfer costs. Smart city applications like autonomous vehicle coordination, real-time traffic management, and emergency response require sub-10ms latency — achievable only with local edge + core data centre architecture. Additionally, data sovereignty laws in many regions require citizen data to remain within local infrastructure.
While AI data centres are power-intensive (40-100 kW per rack), they are significantly more efficient per computation than traditional facilities. AI-optimised cooling achieves PUE (Power Usage Effectiveness) of 1.03-1.1 compared to 1.4-1.6 for air-cooled data centres. Studies have demonstrated up to a 40% reduction in cooling energy through AI optimisation alone. Many new facilities also run on 100% renewable energy.
A digital twin is a real-time virtual replica of the entire city — roads, buildings, utility networks, and data flows — powered by continuous IoT sensor feeds. It allows city planners to simulate changes (new traffic patterns, emergency scenarios, energy grid modifications) before implementing them in the real world, dramatically reducing risk and cost.
Modern AI data centres employ federated learning (training models without centralising raw data), differential privacy (adding mathematical noise to prevent re-identification), edge processing (keeping sensitive data local), and full compliance with regulations like GDPR and SOC 2. Many systems use anonymised and aggregated data rather than individual-level tracking.
Documented returns from smart city deployments include significant annual savings from IoT sensor networks, up to 25% reduction in water consumption, and 30% lower street lighting costs. AI traffic systems have reduced travel times by 25% and emissions by over 20%. Research estimates smart city technologies can improve quality-of-life indicators by 10-30% across mobility, health, safety, and environmental metrics.
Traditional data centre builds take 18-24 months. Modern modular and prefabricated AI data centres can be deployed in 6-12 months. Edge micro data centres can be installed in weeks. Many cities start with a phased approach — deploying edge nodes first for immediate impact, then building core AI compute capacity over 1-3 years as use cases mature.
If you are assessing a smart city deployment, planning a new AI-ready facility, or exploring edge capacity, we can discuss your operational needs and next steps.