Run AI Workloads on Kubernetes — At Scale

GPU scheduling, distributed training, LLM serving with vLLM, and complete MLOps pipelines — designed for GCC enterprises building sovereign AI infrastructure.

Duration: 2-3 months Investment: From $100K Team: 1 AI/ML Engineer + 1 K8s Platform Engineer
The Challenge

You might be experiencing...

GPU costs are unsustainable — no visibility into utilization or waste
ML engineers fighting K8s instead of training models
Need to serve LLMs in-region for data sovereignty but don't know where to start
No MLOps pipeline — models go from notebook to production manually
Our Approach

Engagement Phases

Weeks 1-3

Infrastructure

GPU node pools, NVIDIA GPU Operator, high-performance storage, DCGM monitoring dashboards.

Weeks 3-6

MLOps Pipeline

Kubeflow Training Operator, MLflow experiment tracking, model registry, CI/CD for models.

Weeks 6-9

Model Serving

vLLM or KServe deployment, autoscaling with GPU metrics, load testing, A/B testing.

Weeks 9-12

Optimization & Handover

GPU cost optimization (spot, MIG, right-sizing), documentation, team training, handover.

What You Get

Deliverables

Production-ready GPU K8s cluster with NVIDIA GPU Operator
ML training platform (Kubeflow/Ray with distributed training)
LLM inference serving (vLLM with autoscaling)
MLflow for experiment tracking and model registry
GPU monitoring dashboards (DCGM metrics in Grafana)
MLOps CI/CD pipeline for model deployment
Architecture documentation and operational runbooks
Team training (2-day workshop)
Expected Outcomes

Before & After

MetricBeforeAfter
GPU Utilization25-35%70-85%
Model Deployment TimeDays (manual)Minutes (CI/CD)
Training Job ManagementManual kubectlAutomated with Kueue
LLM Inference LatencyN/AP95 < 500ms
Technology

Tools We Use

NVIDIA GPU Operator vLLM KServe Kubeflow MLflow Ray Kueue
Common Questions

Frequently Asked Questions

How long does it take to build AI/ML infrastructure on Kubernetes?

A typical engagement runs 2-3 months. Weeks 1-3 cover GPU infrastructure setup with NVIDIA GPU Operator, weeks 3-6 build the MLOps pipeline with Kubeflow and MLflow, weeks 6-9 deploy model serving with vLLM, and weeks 9-12 focus on GPU cost optimization and team training.

Can we run LLMs in-region for data sovereignty?

Yes. We specialize in sovereign AI infrastructure for GCC enterprises. We deploy LLM inference serving on Kubernetes clusters within UAE or KSA data centers, ensuring your data never leaves the required jurisdiction while maintaining production-grade performance with P95 latency under 500ms.

How do you optimize GPU costs?

GPU utilization in most organizations sits at 25-35%. We implement spot instances for training jobs, Multi-Instance GPU (MIG) for inference sharing, right-sizing based on actual utilization, and Kueue for intelligent job scheduling. Typical clients see GPU utilization increase to 70-85%.

Do we need Kubernetes expertise on our team?

We handle the Kubernetes complexity so your ML engineers can focus on training models. The engagement includes a 2-day workshop for your team covering day-to-day operations, plus detailed runbooks and documentation. We also offer ongoing managed operations if you prefer.

Which ML frameworks and model serving platforms do you support?

We support distributed training with Kubeflow Training Operator and Ray, experiment tracking with MLflow, job scheduling with Kueue, and model serving with vLLM and KServe. The infrastructure handles PyTorch, TensorFlow, and any framework your ML team uses.

Get Started for Free

We would be happy to speak with you and arrange a free consultation with our Kubernetes Expert in Dubai, UAE. 30-minute call, actionable results in days.

Talk to an Expert