Self-Service GPU Platforms: Building Internal ML Clouds

Updated December 11, 2025

December 2025 Update: Organizations with 8×H100 servers reporting 30-50% GPU utilization under manual allocation—hundreds of thousands wasted. NVIDIA Run:ai acquisition cementing GPU orchestration as critical infrastructure layer. Dynamic fractional GPU sharing eliminating reservation-based inefficiency. Platform abstraction hiding Kubernetes complexity from data scientists.

Data scientists waiting days for GPU access while expensive hardware sits idle represents a failure mode affecting most enterprises with AI ambitions. Traditional IT ticketing systems designed for virtual machine provisioning cannot handle the dynamic, burst-heavy nature of machine learning workloads. Organizations with 8×H100 servers report 30-50% GPU utilization when managed through manual allocation, leaving hundreds of thousands of dollars in compute capacity unused.¹

Self-service GPU platforms transform expensive hardware into internal clouds where data scientists access resources on demand while platform teams maintain governance and cost controls. The approach borrows from cloud-native infrastructure patterns, applying Kubernetes orchestration, fractional GPU sharing, and automated scheduling to GPU clusters. Understanding available platforms and architectural patterns helps enterprises maximize returns on AI infrastructure investments.

The GPU utilization problem

Traditional GPU allocation fails for several interconnected reasons:

Reservation inefficiency: Data scientists request GPUs for project durations measured in weeks, but actual compute usage occurs in bursts. Training runs consume 100% GPU for hours, followed by days of debugging at 0% utilization. Reservation-based systems cannot reclaim idle resources.

Queue friction: When requesting GPUs requires tickets and approvals, teams hoard allocations to avoid future delays. A researcher needing 4 GPUs for a 2-hour experiment won't submit a ticket for such a short duration, instead keeping previously allocated resources reserved.

Visibility gaps: Without real-time utilization metrics, platform teams cannot identify waste or optimize scheduling. Expensive hardware appears "in use" when nothing runs on allocated containers.

Skill mismatch: Data scientists specialize in model development, not Kubernetes manifests or container orchestration. Requiring infrastructure expertise to access compute creates bottlenecks and frustration.

Self-service platforms address these problems through automation, dynamic allocation, and abstraction layers that hide infrastructure complexity from end users.

NVIDIA Run:ai: the enterprise standard

NVIDIA's acquisition of Run:ai cemented GPU orchestration as a critical infrastructure layer. The platform creates virtual GPU pools enabling dynamic, policy-based scheduling across Kubernetes clusters.²

Fractional GPU allocation: Run:ai enables sharing single GPUs across multiple workloads. Jupyter notebooks for exploration might receive 0.25 GPU each, while training jobs receive full GPU or multi-GPU allocations. The fractional approach increases effective cluster capacity by 2-3x for mixed workloads.³

Workload-aware scheduling: Training, fine-tuning, and inference have different resource patterns. Run:ai applies distinct policies for each phase, preempting low-priority inference workloads when training jobs require resources.

Team-based quotas: Organizations define guaranteed resource allocations per team or project using fairshare or hard quota models. Teams receive baseline capacity guarantees while burst capacity draws from shared pools during low-utilization periods.

Enterprise integrations: Run:ai integrates with VMware Cloud Foundation, AWS (EC2, EKS, SageMaker HyperPod), and Azure GPU-accelerated VMs.⁴ The platform works with NVIDIA DGX, DGX SuperPOD, and integrates with NGC containers and NVIDIA AI Enterprise software.

Run:ai licenses per GPU, making cost predictable as clusters scale. Enterprises report 2-3x improvement in effective GPU utilization after deployment, with payback periods measured in months rather than years.

Kubernetes-native alternatives

Organizations with existing Kubernetes expertise can build GPU platforms using open-source components:

Kubeflow for ML workflows

Kubeflow provides the most comprehensive Kubernetes-native MLOps platform, designed for organizations seeking cloud-scale machine learning capabilities.⁵

Kubeflow Pipelines: Workflow orchestration with dependency management, parallel execution, and automatic retries built on Argo Workflows. Teams define ML workflows as code, enabling reproducibility and version control.

Distributed Training Operators: Native support for TensorFlow, PyTorch, and XGBoost distributed training with automatic resource allocation and fault tolerance. Operators handle pod scheduling, gradient synchronization, and checkpoint management.

Katib AutoML: Kubernetes-native hyperparameter tuning, early stopping, and neural architecture search. Katib automates experiment management that would otherwise require manual GPU allocation for each trial.

Kubeflow's strength lies in community governance as a Cloud Native Computing Foundation project with enterprise backing. The complexity trade-off: Kubeflow requires significant Kubernetes expertise to deploy and operate effectively.

ZenML for abstraction

ZenML addresses Kubeflow's complexity by providing abstraction layers that make enterprise-grade infrastructure accessible to ML practitioners:⁶

Multi-orchestrator support: ZenML pipelines deploy on Kubernetes, AWS SageMaker, GCP Vertex AI, Kubeflow, or Apache Airflow without code changes. Teams avoid lock-in while maintaining infrastructure flexibility.

Fractional GPU optimization: Built-in support for GPU sharing and intelligent scheduling reduces infrastructure costs by 30-50% through improved utilization.⁷

Compliance integration: End-to-end lineage tracking and immutable pipeline versions satisfy model risk management requirements. Role-based access control enables multi-tenancy with strict team isolation.

ZenML works well for organizations wanting GPU platform capabilities without building from Kubernetes primitives.

Serverless GPU platforms

External serverless GPU providers complement internal platforms for burst capacity or specialized hardware:

RunPod

RunPod delivers raw GPU compute with pay-per-second billing and minimal infrastructure overhead:⁸

• GPU options from RTX A5000 ($0.52/hour) through H200 ($3-4/hour)
• 48% of serverless cold starts under 200ms
• Container-based deployment with custom image support
• Suitable for batch inference and training overflow

RunPod excels when organizations need flexible access to GPU types not available internally. The platform provides compute without bundled storage, databases, or networking, requiring separate solutions for production environments.

Modal

Modal optimizes for Python-native development with minimal configuration:⁹

• Code-defined infrastructure without YAML manifests
• Pay-per-second billing with automatic scaling
• Cold starts typically 2-4 seconds
• Strong integration with Python ML ecosystem

Modal works best for new AI applications where developers want to avoid infrastructure management entirely. Migrating existing applications or bringing custom containers proves more challenging than on RunPod.

Comparison framework

Organizations typically use serverless platforms for burst capacity exceeding internal cluster limits rather than as primary infrastructure.

Building internal GPU PaaS

Rafay and similar platforms transform existing GPU infrastructure into fully operational GPU PaaS (Platform as a Service) environments:¹⁰

Self-service consumption: Data scientists access GPU resources through portals or APIs without IT tickets. Request-to-provision time drops from days to seconds.

Central orchestration: Platform teams maintain governance, cost controls, and security policies while enabling developer autonomy. Air-gapped deployments support regulated industries.

Multi-tenancy: Teams operate in isolated environments with resource quotas, preventing noisy neighbors while enabling efficient resource sharing.

Application deployment: Beyond raw compute, GPU PaaS platforms bundle common ML applications (Jupyter, training frameworks, inference servers) for one-click deployment.

The transformation typically requires:

1. Kubernetes cluster: GPU-enabled nodes with NVIDIA device plugin and GPU operator
2. Orchestration layer: Run:ai, Rafay, or Kubeflow for scheduling and quota management
3. Storage tier: High-performance shared filesystem for datasets and checkpoints
4. Networking: InfiniBand or high-bandwidth Ethernet for distributed training
5. Monitoring: GPU utilization dashboards and alerting

Architecture patterns

Hub-and-spoke model

Large enterprises often deploy hub-and-spoke architectures:

Central hub: Primary GPU cluster with largest/newest hardware (H100, B200) for production training and inference. Managed by central platform team with strict SLAs.

Regional spokes: Smaller clusters distributed across business units for development and experimentation. Local teams manage within guardrails defined by central governance.

Cloud burst: Overflow capacity from hyperscalers or GPU cloud providers (CoreWeave, Lambda Labs) for peak demand exceeding on-premises capacity.

The model balances cost efficiency of owned hardware with flexibility of cloud burst.

Namespace isolation

Kubernetes namespaces provide logical separation between teams:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: ml-team-quota
  namespace: ml-research
spec:
  hard:
    requests.nvidia.com/gpu: "8"
    limits.nvidia.com/gpu: "16"
    persistentvolumeclaims: "50"

Teams receive guaranteed quotas with burst capacity available when other teams have idle allocations. Run:ai and similar platforms automate quota management with more sophisticated policies than basic Kubernetes ResourceQuota.

Job priority classes

Priority-based scheduling enables preemption for critical workloads:

Production (highest): Inference endpoints serving live traffic. Never preempted.

Training (high): Active model training runs. Preempted only by production.

Development (medium): Jupyter notebooks and interactive development. Preempted by training.

Batch (lowest): Background processing and hyperparameter sweeps. Runs on otherwise-idle resources.

The priority model maximizes utilization while protecting critical workloads.

Implementation roadmap

Organizations building internal GPU platforms should follow a phased approach:

Phase 1: Foundation (4-8 weeks)

• Deploy Kubernetes cluster with GPU nodes
• Install NVIDIA GPU Operator and device plugin
• Configure basic namespace isolation
• Implement monitoring (Prometheus, Grafana, DCGM exporter)

Phase 2: Orchestration (4-6 weeks)

• Deploy Run:ai, Kubeflow, or ZenML
• Define team quotas and scheduling policies
• Build self-service portal or integrate with existing tools
• Train data scientists on new workflows

Phase 3: Optimization (ongoing)

• Analyze utilization patterns and adjust quotas
• Implement fractional GPU sharing for appropriate workloads
• Add cloud burst integration for peak capacity
• Automate common deployment patterns

Phase 4: Advanced capabilities

• Distributed training automation
• Model registry integration
• CI/CD for ML pipelines
• Cost allocation and chargeback

Organizations deploying GPU infrastructure can leverage Introl's expertise for hardware deployment and platform configuration across 257 global locations.

Cost allocation and chargeback

Platform success requires visibility into resource consumption by team, project, or workload:

GPU-hours tracking: Record actual GPU time consumed per namespace or user. The metric enables fair cost allocation even with dynamic scheduling.

Utilization reporting: Track GPU utilization percentage alongside allocation. Teams consuming 1000 GPU-hours at 20% utilization have different optimization opportunities than teams at 80%.

Chargeback models: - Direct chargeback: Teams pay for consumed GPU-hours at internal transfer price - Showback: Teams see consumption but central budget absorbs cost - Quota-based: Teams receive budgeted allocation; additional capacity charged

Most organizations start with showback to build awareness before implementing chargeback that affects team budgets.

Platform selection criteria

Evaluate GPU platforms against organizational requirements:

Existing infrastructure: - VMware environment → Run:ai with VCF integration - AWS-heavy → SageMaker or Run:ai on EKS - Strong Kubernetes team → Kubeflow or ZenML - Limited infrastructure expertise → Managed platforms (Rafay, Saturn Cloud)

Workload characteristics: - Training-heavy → Prioritize distributed training support - Inference-heavy → Evaluate autoscaling and fractional GPU - Mixed → Workload-aware scheduling critical

Compliance requirements: - Air-gapped → On-premises platforms only - Multi-tenant isolation → Run:ai or platforms with strong RBAC - Audit trails → ZenML or platforms with lineage tracking

Team composition: - Infrastructure engineers available → Open-source viable - ML engineers only → Managed platforms reduce burden - Both → Layered approach with managed foundations

The platform team imperative

Self-service GPU platforms shift IT from gatekeepers to enablers. Platform teams transition from manually processing requests to building automation, improving utilization, and reducing friction. The 96% of enterprises running production workloads on Kubernetes, according to CNCF surveys, indicates organizational readiness for cloud-native GPU management.¹¹

The investment pays dividends quickly. Organizations report 2-3x GPU utilization improvement within months of deployment, effectively doubling or tripling effective cluster capacity without hardware purchases. Data scientist productivity increases as request-to-compute latency drops from days to seconds.

For enterprises investing millions in GPU infrastructure, self-service platforms represent essential optimization. The alternative—expensive hardware sitting idle while data scientists wait in queues—wastes both capital and human potential. Modern orchestration platforms make GPU clouds as accessible as virtual machines became a decade ago, and organizations that master internal GPU platforms gain competitive advantage in the race to deploy AI at scale.

References

1. ZenML. "Managing MLOps at Scale on Kubernetes: When Your 8×H100 Server Needs to Serve Everyone." ZenML Blog. 2025. https://www.zenml.io/blog/managing-mlops-at-scale-on-kubernetes-when-your-8xh100-server-needs-to-serve-everyone

2. NVIDIA. "Accelerate AI & Machine Learning Workflows | NVIDIA Run:ai." Accessed December 8, 2025. https://www.nvidia.com/en-us/software/run-ai/

3. NVIDIA. "NVIDIA to Acquire GPU Orchestration Software Provider Run:ai." NVIDIA Blog. 2024. https://blogs.nvidia.com/blog/runai/

4. VMware. "Enterprise customers can now deploy NVIDIA Run:ai on VMware Cloud Foundation." VMware Cloud Foundation Blog. November 7, 2025. https://blogs.vmware.com/cloud-foundation/2025/11/07/enterprise-customers-can-now-deploy-nvidia-runai-on-vmware-cloud-foundation/

5. Collabnix. "Kubernetes and AI: Mastering ML Workloads in 2025." 2025. https://collabnix.com/kubernetes-and-ai-the-ultimate-guide-to-orchestrating-machine-learning-workloads-in-2025/

6. ZenML. "One AI Platform - From Pipelines to Agents." Accessed December 8, 2025. https://www.zenml.io/

7. ———. "Managing MLOps at Scale on Kubernetes."

8. RunPod. "Top Serverless GPU Clouds for 2025: Comparing Runpod, Modal, and More." 2025. https://www.runpod.io/articles/guides/top-serverless-gpu-clouds

9. Modal. "Top 5 serverless GPU providers." Modal Blog. 2025. https://modal.com/blog/serverless-gpu-article

10. Rafay. "Enterprise GPU PaaS for AI Workloads." Accessed December 8, 2025. https://rafay.co/platform/enterprise-gpu-paas

11. Cloud Native Now. "Why Kubernetes is Great for Running AI/MLOps Workloads." 2025. https://cloudnativenow.com/contributed-content/why-kubernetes-is-great-for-running-ai-mlops-workloads/

Key takeaways

For finance teams: - Organizations report 30-50% GPU utilization with manual allocation → 2-3x improvement with self-service platforms - Run:ai fractional GPU (0.25 GPU for notebooks) increases effective capacity 2-3x for mixed workloads - ZenML fractional optimization reduces infrastructure costs 30-50%; licenses per GPU enables predictable scaling

For platform teams: - Run:ai is enterprise standard (NVIDIA acquired); integrates VMware VCF, AWS (EC2, EKS, SageMaker), Azure GPU VMs - Kubeflow provides comprehensive Kubernetes-native MLOps but requires significant K8s expertise to operate - 96% of enterprises run production on Kubernetes (CNCF); ready for cloud-native GPU management

For ML engineers: - Serverless burst: RunPod $0.52-4/hr with 48% cold starts <200ms; Modal Python-native 2-4s cold starts - ZenML pipelines deploy on Kubernetes, SageMaker, Vertex AI, Kubeflow, or Airflow without code changes - Priority scheduling: Production (never preempted) → Training → Development → Batch (idle resources only)

For operations teams: - Hub-and-spoke architecture: Central GPU cluster (H100/B200) + regional spokes + cloud burst for peak demand - Implementation: Phase 1 (4-8 weeks) foundation, Phase 2 (4-6 weeks) orchestration, Phase 3 optimization (ongoing) - Chargeback models: Direct (pay for GPU-hours), Showback (visibility only), Quota-based (budget + overage charges)

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Self-service GPU platforms boost utilization 2-3x by eliminating ticket queues. Complete guide to Run:ai, Kubeflow, and building internal ML cloud infrastructure.

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Self-Service GPU Platforms: Internal ML Cloud Guide 2025

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Build internal GPU clouds with Run:ai, Kubeflow, and ZenML. Achieve 2-3x utilization improvement through self-service access and intelligent orchestration.

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