AI is no longer a science project in a back corner of the enterprise. It’s running in production, powering everything from personalized recommendations to complex industrial design. The infrastructure that supports these workloads, often called AI factories, has become the modern engine of innovation. Through our collaboration with NVIDIA, Virtana is bringing deep, real-time observability to these environments so IT teams can maximize performance, efficiency, and sustainability.
But running an AI factory is nothing like running a traditional IT environment. It’s not enough to have racks of GPUs humming away. Without deep, real-time observability into every layer of the stack, IT teams can’t keep pace with the demands of large-scale AI workloads. Performance bottlenecks, idle GPUs, and runaway costs become the norm, not the exception.
Just as the industrial factories of the past century relied on precise monitoring and optimization to achieve peak output, AI factories need the same kind of intelligence to deliver results. Observability is that missing link, the foundation that turns raw compute power into sustainable, scalable AI outcomes.
This post breaks down what makes AI factories unique, why observability is a competitive imperative, and how the right approach can turn infrastructure from a liability into a growth accelerator.
What Is an AI Factory and Why It’s Different
An AI factory is more than a data center with a few GPUs bolted on. It’s a tightly integrated environment that handles the entire AI lifecycle: ingesting massive datasets, training large models, performing inference at scale, and continuously refining those models for accuracy and efficiency.
Key differences from traditional IT environments include:
- Multi-layered complexity: AI factories involve interdependent systems — from applications to GPUs to storage to the network — that must operate in perfect sync.
- Dynamic workloads: Training and inference jobs can shift rapidly between on-premises servers, cloud resources, and containerized environments.
- Performance sensitivity: Even small delays can cause cascading effects, disrupting production pipelines or service-level agreements.
- Specialized hardware: High-performance GPUs, often from NVIDIA, are the beating heart of AI factories. Without the right visibility into how they’re used, optimization is guesswork.
These differences create an environment where traditional monitoring tools fall short. In a conventional data center, it’s enough to watch CPU load or network throughput. In an AI factory, you need to see how every layer interacts in real time and you need to be able to predict problems before they impact production.
For a deeper dive into why AI is changing the business landscape, read Every Company Is Now an AI Company: 5 Observability Must-Dos to Win in the Age of Intelligence.
The Observability Gap in AI Factories
The problem isn’t that AI factories lack data, it’s that they generate too much of it in too many places. GPU utilization stats live in one system, application logs in another, network metrics somewhere else. Stitching all of that together to find the source of a slowdown can take hours, time that AI workloads can’t afford.
Why traditional monitoring falls short:
- Lack of GPU-level visibility: Most tools weren’t designed to track specialized AI hardware. They might show GPU temperature or uptime, but not throttling behavior, memory contention, or per-job utilization.
- No unified view: Without a single pane of glass, IT teams are left correlating data from multiple dashboards, increasing mean time to resolution (MTTR).
- Tool sprawl and siloed data: Multiple overlapping tools lead to confusion, duplicate alerts, and blind spots in performance data.
- Reactive approach: Issues are often discovered only after they’ve already impacted operations or caused costly delays in training cycles.
Gartner recently projected that by 2029, 70% of large enterprises failing to effectively utilize AI factories will cease to exist. That’s not hyperbole — it’s a recognition that the AI era will create clear winners and losers, and infrastructure readiness will be a deciding factor.
See Gartner Warns AI Factories Will Determine Market Winners by 2029 for a deeper look at the research.
Without a comprehensive observability strategy, AI factories risk becoming black boxes, consuming vast amounts of capital and energy without delivering proportional business value.
Risks of Flying Blind
Running an AI factory without deep observability is like operating a manufacturing plant with the lights off. You might still produce output, but inefficiency, waste, and breakdowns will go unnoticed until they become expensive problems.
Key risks include:
- Resource contention and idle GPUs
- Some jobs may sit idle without visibility into GPU scheduling, while others choke on insufficient resources.
- Idle GPUs still consume power and rack space, driving up operational costs without delivering ROI.
- Longer mean time to resolution (MTTR)
- When performance dips, teams spend precious hours correlating siloed data instead of fixing the problem.
- In AI workloads, delays in root cause analysis can disrupt training timelines or delay time-to-market.
- Missed SLAs for AI-powered services
- Inference latency or degraded model performance can directly impact user experience and breach contractual service-level agreements.
- Sustainability setbacks
- Overprovisioned hardware and inefficient workloads waste energy, increasing environmental impact and jeopardizing ESG goals.
NVIDIA founder and CEO Jensen Huang put it succinctly during his GTC 2025 keynote:
“AI infrastructure must account for more than just raw performance, it must also consider energy consumption, physical space, and operational costs. Optimizing workloads to use only the compute resources truly necessary will be critical for scaling AI responsibly.” Press Release
In other words, observability isn’t just about keeping the lights on. Instead, it’s about scaling AI in a financially and environmentally sustainable way.
Why GPU Telemetry Is the Starting Point
In AI factories, GPUs are the beating heart. They handle the heaviest lifting in training and inference and represent some of the most expensive infrastructure investments. Yet in many organizations, GPU usage is still a black box.
What GPU telemetry delivers:
- Utilization data: showing how much of the GPU’s compute power is actually being used.
- Throttling insights: revealing when thermal or power constraints are slowing performance.
- Power draw and temperature: helping manage both cost and sustainability metrics.
- Per-job metrics: understanding how specific workloads impact performance and capacity.
Why it matters:
- Identifies underutilized GPUs so resources can be reallocated.
- Flags performance anomalies before they snowball into outages.
- Informs capacity planning so teams buy only what they need — and get the most out of what they already own.
For a deep dive on this topic, see AI Factory Observability Starts with GPU Telemetry.
Observability starts at the GPU layer because that’s where performance, cost, and sustainability intersect most directly in AI factories. But the real value comes when GPU telemetry is correlated with application, storage, and network data, and that’s where Virtana and NVIDIA step in together.
Closing the Gap: Virtana + NVIDIA
Virtana’s collaboration with NVIDIA delivers an observability foundation purpose-built for the unique demands of AI factories. By combining Virtana’s AI-powered platform with NVIDIA’s accelerated computing technologies, IT teams gain deep, actionable insights across every layer of their AI infrastructure from applications to GPUs to the underlying storage and network fabric.
Here’s how the Virtana Platform’s key capabilities translate into real-world impact:
1. Automated Topology Discovery
What it is: Automatically maps interdependencies between AI applications, NVIDIA GPUs, storage systems, and network components in real time.
Why it matters:
- In AI factories, workloads are highly dynamic, often shifting between on-prem, cloud, and containerized environments.
- Real-time mapping helps teams quickly understand where workloads run, what resources they consume, and how changes ripple through the environment.
NVIDIA tie-in:
- Integrates with NVIDIA GPU telemetry to include GPU health, utilization, and performance metrics directly in the topology map.
2. AI-Based Root Cause Analysis
What it is: Uses machine learning, enhanced by NVIDIA AI Enterprise, to pinpoint the root cause of performance issues in seconds, even in massive, distributed AI environments.
Why it matters:
- Reduces mean time to resolution (MTTR) by quickly identifying whether the issue is in the application, GPU, storage, or network.
- Prevents the “war room” effect, where teams waste hours debating the source of a problem.
NVIDIA tie-in:
- Leverages GPU workload data and inference pipeline telemetry to detect GPU throttling, bottlenecks, or misconfigured workloads.
3. Predictive Performance Management
What it is: Analyzes historical and real-time data to forecast when and where performance bottlenecks will occur, so teams can address them before they impact service.
Why it matters:
- AI factories must operate with zero tolerance for downtime in training and inference cycles.
- Anticipating problems before they happen means avoiding costly service interruptions or retraining delays.
NVIDIA tie-in:
- Predictive models can factor in GPU load patterns, model size, and inference token usage for more accurate performance forecasting.
4. Cost and Capacity Optimization
What it is: Uses AI-driven insights to right-size infrastructure usage and avoid overprovisioning, a common issue in GPU-heavy AI environments.
Why it matters:
- GPUs are expensive to buy and operate; idle or underutilized GPUs directly increase operating costs.
- Balances workload demands with available resources for both cost control and sustainability.
NVIDIA tie-in:
- Integrates NVIDIA GPU metrics into capacity planning, ensuring organizations purchase only the compute power they truly need.
5. OpenTelemetry-Based Monitoring for NVIDIA NIM
What it is: Delivers deep observability for NVIDIA Inference Microservices (NIM) using OpenTelemetry standards to monitor, trace, and optimize AI workloads.
Why it matters:
- NIM is designed for scalable AI inference, but without integrated observability, token usage and performance inefficiencies can go undetected.
- OpenTelemetry ensures consistent, vendor-neutral monitoring across hybrid environments.
NVIDIA tie-in:
- Brings full-stack observability, from inference requests to GPU execution, directly into Virtana’s platform.
In short, Virtana’s integration with NVIDIA GPUs provides an operational advantage. Together, they enable IT teams to run AI factories at industrial scale, with the confidence that performance, cost, and sustainability are all moving in the right direction.
The Competitive Imperative
The AI era will separate market leaders from laggards faster than any previous technology wave. Without observability, AI factories become expensive experiments. They consume capital, power, and floor space without delivering consistent business value. With observability, they become engines of competitive advantage, enabling:
- Faster time-to-market by minimizing downtime and accelerating troubleshooting.
- Higher service quality by maintaining consistent performance for AI-powered applications.
- Better resource utilization by ensuring every GPU, watt of power, and rack unit is used efficiently.
- Sustainable scaling by balancing compute performance with environmental and budget constraints.
As Gartner warns, by 2029, 70% of large enterprises failing to effectively utilize AI factories will cease to exist. That’s a stark reminder: AI success is no longer optional — and infrastructure readiness is a competitive necessity.
Next Steps
The rise of AI factories marks a turning point for enterprise IT. The winners of the next decade won’t be those who deploy AI; they’ll be the ones who can operate it at scale, day in and day out, with precision and efficiency.
Observability is the missing link that makes this possible. It transforms AI factories from high-cost infrastructure experiments into high-performing, sustainable engines of innovation.
Through our collaboration with NVIDIA, Virtana is helping enterprises achieve exactly that. By unifying observability across AI-native workloads, GPUs, and the entire hybrid environment, we give IT teams the intelligence they need to support AI at industrial scale.
Ready to see how AI factory observability can transform your operations?
- Learn more about Virtana AI Factory Observability
- Read how GPU telemetry is changing AI infrastructure management
- Understand why AI infrastructure readiness will determine market winners by 2029
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