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Telcos Are Wasting 50% of Their GPU Capacity - Here’s How to Fix It

  • phuongthieu
  • 9 hours ago
  • 3 min read


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What Is GPU Underutilization in Telecom?

GPU underutilization occurs when powerful graphics processing units deployed across telco cloud and edge infrastructure are not used to their full capacity. These GPUs power AI-driven applications, network optimization, real-time analytics, and edge computing workloads.

Yet, many telcos face a common challenge: expensive GPU infrastructure sits idle, driving up operational costs while failing to deliver ROI.


Why Underutilized GPUs Are a Growing Problem for Telcos

As telcos expand AI services and adopt edge computing, underutilized GPUs represent a silent cost. Idle GPUs consume power, cooling, and licensing without providing value.

For operators, this inefficiency limits:

  • Scalability of AI/ML services

  • Operational efficiency across distributed infrastructure

  • Speed of innovation for new digital services


Industry Data: Telcos Use Only 30-50% of Their GPU Capacity

According to RCR Wireless News, telecommunications companies use just 30-50% of their GPU resources.

This means over half of the GPU investment remains untapped, a costly problem in a market where operators must deliver AI-driven services at scale while managing operational expenses.

Causes of GPU Underutilization in Telco Cloud Environments

  • Siloed Edge and Cloud Infrastructure

Edge nodes and central cloud clusters often operate independently, preventing GPUs from being shared across the network.

  • Static Resource Provisioning

GPUs are traditionally tied to specific workloads. When demand decreases, the hardware remains idle.

  • Unpredictable AI/ML Workload Patterns

Inference and training workloads fluctuate throughout the day. Without dynamic scheduling, GPU cycles go unused.

  • Over-Provisioning for Reliability

Operators frequently deploy more GPUs than needed to guarantee uptime, leaving significant capacity unused.

  • Lack of Elastic GPU Scaling

Without cloud bursting, telcos can’t offload excess workloads, forcing permanent over-provisioning.

The Business Impact of Idle GPU Resources

  • Higher operational costs: Power, cooling, and maintenance continue even for idle hardware.

  • Slower rollout of AI/ML services: Capacity constraints slow innovation.

  • Lower ROI on GPU investments: Millions spent on infrastructure do not deliver value.

  • Competitive disadvantage: Operators with optimized GPU utilization move faster in AI adoption.

How SkyLab Solves the GPU Underutilization Challenge

SkyLab provides a cloud-native orchestration layer tailored for telcos, transforming idle GPUs into productive, high-performance infrastructure.

  • Dynamic GPU Scheduling Across Edge and Central Nodes

SkyLab continuously monitors workload demand and allocates tasks to the most suitable GPU resources, whether at the edge or in central cloud clusters.

Benefits:

  • Improved workload distribution

  • Higher real-time GPU utilization

  • Reduced idle time

Cloud Bursting for Flexible AI/ML Scaling

When local GPU resources are maxed out, SkyLab automatically extends workloads into external cloud GPUs.

Benefits:

  • Elastic scaling without over-provisioning

  • Reduced infrastructure costs

  • Faster deployment of AI/ML services

Measurable Business Outcomes for Telcos

  • Lower idle GPU costs: Every GPU cycle delivers value, reducing OPEX.

  • Higher resource efficiency: Intelligent scheduling maximizes hardware utilization.

  • Flexible AI/ML scaling: Launch AI workloads faster and without bottlenecks.

  • Faster innovation cycles: Teams can deploy new services more efficiently.

Why Optimizing GPU Utilization Is Now a Strategic Priority

AI adoption in telecommunications is accelerating, but underutilized GPUs limit performance and innovation. By implementing dynamic GPU scheduling and cloud bursting, telcos can:

  • Reduce operational costs

  • Improve AI/ML workload efficiency

  • Scale services rapidly across edge and central nodes

The ability to fully utilize GPU infrastructure is no longer optional - it’s a strategic advantage for telcos competing in the AI-driven market.


Ready to maximize your GPU infrastructure and scale AI faster? Contact SkyLab today to see how dynamic scheduling and cloud bursting can transform your telco network.

 
 
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