GPU Infrastructure: Automation and Strategy

How to design, automate, and scale GPU infrastructure for AI workloads while improving utilization and reducing fragmentation across clusters and clouds.

AI is driving a sharp shift toward GPU-first infrastructure, but most organizations are still struggling to use that capacity efficiently. Datadog's State of Cloud Costs 2024 found that organizations using GPU instances increased spending on them by 40% in the last year as more teams experimented with large language models and other AI workloads. The same report notes GPUs can be more than 200% faster than CPUs for training LLMs and parallel AI workloads, which makes them critical to the stack. Getting value from that investment comes down to orchestration and scheduling. Industry research consistently shows the same pattern: rising GPU spend alongside idle or fragmented capacity, queue contention, and weak peak utilization—more often an operational problem than a simple lack of hardware. This article explains what GPU infrastructure is, how automation helps, and how to implement and run it so your fleet delivers.

Key highlights:

• GPU infrastructure is the combination of GPU hardware, orchestration software, and tooling used to run AI and high-performance computing workloads at scale. It spans on-premises clusters, cloud instances, and hybrid setups.

• Unifying allocation and compute in a single platform reduces the operational drag behind that utilization gap—so teams spend less time fighting fragmentation and idle capacity.

• A practical path runs from cluster design and Kubernetes-native GPU orchestration through monitoring and hybrid or multi-cloud. Matching infrastructure to the three core AI workloads (data preparation, training, inferencing) and planning integration with existing tools keeps strategy aligned with outcomes.

• Mirantis' k0rdent AI is designed to provide a common control plane for GPU and AI workloads across data center, cloud, and edge, so teams can focus on building rather than managing infrastructure.

What Is GPU Infrastructure?

GPU infrastructure is the hardware, orchestration layer, and operational tooling you use to provision, schedule, and run AI and high-performance computing (HPC) workloads on GPUs. It includes the GPUs themselves (on-prem or in the cloud), the software that exposes them to workloads (e.g., device plugins and operators in Kubernetes), and the practices and platforms used to manage capacity, utilization, and lifecycle.

The stakes are high. An AIIA and ClearML survey (March 2024, 1,000+ enterprises) found that 96% of companies plan to expand AI compute capacity and investment. Turning that expansion into consistent throughput—not more stranded capacity—is where automation and a clear strategy matter.

How GPU Cloud Infrastructure Benefits from Automation

The scheduling and allocation side of that gap shows up clearly in survey data. Most teams hit the same ceiling: job placement and allocation cannot keep up with demand. ClearML's State of AI Infrastructure at Scale 2024 (with AIIA and FuriosaAI) found 74% of companies dissatisfied with their current scheduling tools and facing allocation constraints regularly, with limited on-demand and self-serve access to GPU compute. Only 7% of respondents achieve more than 85% GPU utilization during peak periods; 53% sit at 51–70% and 15% below 50%. The same report finds that 93% believe team productivity would rise if real-time compute could be self-served easily, and 74% see value in unifying compute and scheduling in one platform (while only 19% have a tool that supports queue visibility and optimization). A single platform that unifies allocation and visibility closes that gap.

Improve GPU Utilization and Resource Allocation

When GPUs are scheduled and shared in a structured way, utilization rises. A common approach is to enforce quotas, surface idle capacity, and route workloads to the right nodes so expensive hardware is not left idle. In the ClearML survey, 40% of organizations said they plan to lean on orchestration and allocation to maximize existing infrastructure. In practice, that means moving from ad hoc allocation to predictable, policy-driven placement that matches demand.

• A single control plane for scheduling and compute keeps capacity and jobs visible and manageable in one place.

• Expose self-serve GPU access where appropriate, so data science and ML teams can run workloads without constant ticket-based provisioning.

Reduce Manual Infrastructure Management

Manual provisioning and hand-built scheduling do not scale. In practice, automated tooling reduces toil and configuration drift so platform teams can focus on reliability and optimization instead of one-off fixes. Integration with GitOps and existing enterprise tooling (e.g., Terraform) keeps GPU infrastructure consistent and auditable.

Accelerate AI Model Training and Deployment

Faster, repeatable pipelines for training and deployment depend on predictable GPU access and consistent environments. This typically involves ensuring jobs get the right GPU types and sizes and that queues are fair and visible, so model training and inference can be scheduled and scaled without manual intervention.

Support Multi-Tenant AI Workloads

Multi-tenancy requires isolation, quotas, and fair sharing. Automated placement and resource management enforce those boundaries so multiple teams or projects can share clusters safely. Technologies such as time-slicing or multi-instance GPUs (where supported) let multiple workloads share a GPU in a controlled way, raising utilization without sacrificing isolation where it matters.

Scale GPU Infrastructure for Enterprise AI

Enterprises need to scale out (more GPUs) and scale efficiently (better use of what they have). A unified orchestration layer supports both: adding capacity in a consistent way and adopting a GPU cloud and AI factory style approach that spans clusters and clouds. As demand grows, that same system can extend across hybrid and multi-cloud so strategy stays consistent as footprint expands.

Key Steps to Implement an Automated AI GPU Infrastructure

Once the need for automation is clear, the next step is implementation. Automated GPU infrastructure typically follows a sequence from design through orchestration, allocation, monitoring, and hybrid or multi-cloud extension. Getting the architecture and workload fit right up front avoids rework later.

1. Design a Scalable GPU Cluster Architecture

Cluster design sets the ceiling for performance and operability. Gartner guidance on GPUs in the datacenter (Computer Weekly, 2025) notes that Ethernet can be a viable alternative to InfiniBand for GPU clusters up to several thousand GPUs, citing reliability, performance, and a broad supplier ecosystem—while acknowledging that some HPC-style workloads may still warrant other interconnect choices. Dedicated physical switches for GPU connectivity are preferable to reusing general-purpose datacenter switches. In AI buildouts, networking often represents 15% or less of cost, and reusing existing switches frequently leads to suboptimal price/performance. For clusters below 500 GPUs, one or two physical switches are often enough; for larger scale, a dedicated AI Ethernet fabric (e.g., middle-of-row or modular switching) is appropriate. Co-certified implementations (networking and GPU vendors) reduce risk and mean time to repair.

• Size and topology matter. Minimize hops and choose topologies (e.g., single-switch, two-switch, full-mesh) that suit your scale and traffic patterns.

• Plan for observability. Sub-second telemetry and real-time alerting for bandwidth, packet loss, jitter, and latency help you run and troubleshoot GPU clusters.

2. Deploy Kubernetes-Native GPU Orchestration

Kubernetes has stable support for scheduling GPUs via device plugins. You expose GPUs as schedulable resources (e.g., nvidia.com/gpu or amd.com/gpu) and pods request them in the same way they request CPU or memory. GPUs must be specified in the limits section only; when both requests and limits are used, they must be equal. Install the appropriate device plugin and drivers for your GPU vendor (NVIDIA, AMD, Intel); then rely on node labels and Node Feature Discovery (NFD) so the scheduler can place workloads on the right GPU types and sizes.

• Node labels and selectors direct workloads that need a specific GPU type or size to the correct nodes.

• Consider the GPU operator for your vendor (e.g., NVIDIA GPU Operator) to automate driver and plugin lifecycle.

3. Implement GPU Scheduling and Resource Allocation

Allocation policy should match how you run AI workloads. Forrester's 2024 evaluation of AI infrastructure solutions frames AI infrastructure around three core workloads: data preparation, training, and inferencing. Deep learning and LLMs typically need GPUs; some predictive workloads may not. In practice, teams often use different providers or clusters for different workloads (e.g., on-prem for data, a cloud provider for inference). Your strategy should reflect that: quotas, priorities, and placement rules that fit each workload type, plus integration of a vendor's AI infrastructure management with your existing tooling (monitoring, access control, provisioning, cost optimization).

• Define quotas and priorities so training and inference (and different teams) get fair, predictable access.

• Plan integration with existing observability, IAM, and cost management so that GPU infrastructure is not an island.

4. Automate Monitoring and Performance Optimization

Continuous monitoring of utilization, job runtimes, and queue health is the basis for optimization. Metrics from the GPU operator or device plugins (e.g., NVIDIA DCGM) surface utilization, memory, and errors. Automate alerting and, where possible, scaling or rebalancing so that underused nodes or stranded capacity are surfaced and addressed. That closes the loop between "design and deploy" and "run and improve."

5. Enable Hybrid and Multi-Cloud GPU Infrastructure

GPU workloads often run in more than one place: on-prem, in a single cloud, or across multiple clouds. Forrester's 2024 takeaways on AI infrastructure note that organizations often use different providers for different workloads. A hybrid cloud strategy (consistent orchestration and policy across environments) reduces lock-in and lets you place workloads where cost, compliance, or latency matter most. Automation and a unified orchestration layer make that feasible at scale.

Best Practices for Managing GPU Cluster Infrastructure at Scale

How you operate that infrastructure matters as much as how you design it. The same utilization dynamics—idle accelerators and stranded capacity—carry a financial angle: TechInsights estimated that in 2023, 878,000 accelerators generated roughly seven million GPU-hours and about $5.8 billion in revenue spending; The Register estimates that if clusters operated near capacity, revenue would be substantially higher. The following practices help you close the loop operationally.

Monitor GPU Utilization and Performance Continuously

You cannot improve what you do not measure. GPU utilization and performance metrics from your orchestration layer and GPU operators surface utilization, queue depth, and job mix. The NVIDIA GPU Operator supports time-slicing so multiple pods can share a GPU and interleave workloads, which can improve throughput on older or shared nodes. Set targets (e.g., peak utilization or fairness) and alert when metrics or wait times drift.

• Track use and queue metrics so bottlenecks and idle capacity are visible.

• Sharing mechanisms (e.g., time-slicing or MIG) can raise throughput without over-provisioning where appropriate.

Prevent GPU Fragmentation Across Clusters

Fragmentation (small pockets of free capacity that cannot satisfy larger requests) wastes GPUs. Centralized or federated placement, bin-packing policies, and placement rules that prefer consolidating workloads on fewer nodes can reduce fragmentation. Simplified cluster management that treats multiple clusters as one logical pool helps as well.

Balance Training and Inference Workloads

Training and AI inference have different latency and throughput needs. Quotas, priorities, and separate queues or node pools help ensure batch training does not starve inference (or the reverse). Clear policies and automation make it easier to balance both on the same infrastructure.

Implement Resource Quotas and Workload Isolation

Quotas and isolation protect tenants and prevent runaway jobs from consuming the whole cluster. In practice, that means enforcing limits per team, project, or namespace and applying isolation (e.g., namespaces, network policies, or GPU partitioning where available) so workloads do not interfere. Automated enforcement keeps quotas consistent and auditable.

Streamline Scaling and Lifecycle Management

Scaling (adding or removing nodes) and lifecycle (driver and plugin upgrades, node replacement) should be repeatable and, where possible, automated. GitOps and declarative config keep changes versioned and rolled out consistently. A platform that abstracts multi-cluster and multi-cloud can simplify scaling and lifecycle across environments.

Automate GPU Cluster Management with Mirantis

To address these challenges in practice, many organizations adopt platforms that unify GPU and AI workload management. Such a platform brings those workloads under one control plane across data center, cloud, and edge. Mirantis' k0rdent AI is designed to serve that role. The same pattern shows up in partner ecosystems: NVIDIA's DGX Cloud Serverless Inference, for example, abstracts multi-cluster infrastructure across multi-cloud and on-premises with a single API, global load-balancing, and autoscaling so teams focus on AI innovation rather than infrastructure.

• One control plane for all clusters and environments so operations, policy, and visibility stay consistent across data center, cloud, and edge.

• Multi-cluster and multi-cloud in one place, with secure multi-tenant access, observability, and cost controls.

• Repeatable provisioning and orchestration (including drift prevention), often via GitOps, so changes are auditable.

• Visibility into usage, quotas, and cost so teams can control spend and access.

• Coverage from hardware to workloads: multi-tenancy, hybrid and multi-cloud portability, and options for sovereign or compliance-sensitive deployments.

Book a demo to see how Mirantis can streamline GPU infrastructure management for your enterprise.

Frequently Asked Questions

What Are the Key Components of GPU Infrastructure?

GPU infrastructure includes the GPU hardware (cards, nodes, or cloud instances), the software that exposes and schedules GPUs (e.g., Kubernetes device plugins and operators), and the tooling and practices for monitoring, allocation, and lifecycle. Networking and storage (for datasets and checkpoints) are also part of the picture. Design and operations should align with the main AI workload types: data preparation, training, and inferencing.

How Does GPU Scheduling Work in Kubernetes Clusters?

In Kubernetes, GPUs are scheduled as custom resources (e.g., nvidia.com/gpu) advertised by device plugins. Pods request GPUs in the limits section of the container spec; the scheduler places pods on nodes that have the requested GPU type and capacity. Node labels and Node Feature Discovery help match workloads to the right GPU types. For deeper integration with scaling and lifecycle, platforms such as Mirantis' k0rdent AI can add a control plane that spans clusters and clouds.

What Challenges Arise When Scaling AI GPU Infrastructure?

Scaling runs into utilization and allocation challenges: underused capacity, fragmentation, and contention when many jobs need GPUs at once. ClearML's State of AI Infrastructure at Scale 2024 shows only a small minority of firms achieving high peak utilization; The Register's coverage of cloud GPU deployment points to widespread underused accelerators. Both point to resource stranding and queue management as often as to a lack of hardware. Automation, a unified view of capacity and jobs, and practices such as quotas, sharing (e.g., time-slicing), and hybrid or multi-cloud placement help address them.

What Is the Difference Between GPU Infrastructure and GPU as a Service?

GPU infrastructure is the broad set of hardware, software, and practices you use to run GPU workloads (whether you build and operate it yourself or consume it as a service). GPU as a Service (GPUaaS) is a consumption model where a provider operates the GPUs and often the orchestration layer; you consume capacity via API or console. Enterprises often use both: their own GPU infrastructure for some workloads and GPUaaS for burst, specific regions, or lower operational load.

Can GPU Cluster Infrastructure Run Across Hybrid or Multi-Cloud Environments?

Yes. GPU clusters can run in your data center, in one or more public clouds, or in a combination. The main challenges are consistent orchestration, scheduling, and policy. A centralized management layer and multi-cloud management practices (and platforms that support them) make it feasible to run GPU cluster infrastructure across hybrid and multi-cloud so workload placement can follow cost, compliance, and latency needs.