AI & ML Infrastructure

Running AI at scale means GPUs, orchestration and cost discipline. We build and operate ML infrastructure on Kubernetes, from GPU cluster provisioning to model serving, so your AI team ships models while costs stay under control, on infrastructure you own.

GPU & Compute

• NVIDIA GPU Operator - Automated GPU driver, container toolkit, and device plugin management on Kubernetes
• NVIDIA DCGM Exporter - GPU metrics collection for Prometheus (utilization, memory, temperature, power)
• Node Feature Discovery (NFD) - Automatic detection and labeling of GPU nodes
• Time-slicing & MIG - Multi-instance GPU partitioning for improved utilization
• CUDA workload scheduling - Tolerations, node selectors, and resource limits for GPU pods

Autoscaling

• Karpenter - Just-in-time node provisioning with GPU-aware instance selection
• Cluster Autoscaler - Node pool scaling based on pending pod requests
• KEDA - Event-driven pod autoscaling based on queue depth, HTTP requests, or custom metrics
• Horizontal Pod Autoscaler (HPA) - CPU/memory and custom metrics scaling for inference workloads
• Provisioner configurations - Spot vs on-demand strategies, instance type constraints, taints

Cost Optimization

• Spot/Preemptible instances - GPU workloads on spot with checkpointing and graceful termination
• Kubecost / OpenCost - Per-namespace and per-workload cost attribution
• Right-sizing - Resource request tuning based on actual utilization metrics
• Idle GPU detection - Alerts for underutilized GPU nodes
• Reserved capacity planning - Savings plans and committed use discounts for baseline workloads

ML Workflow & Orchestration

• Argo Workflows - DAG-based ML training pipelines with artifact passing
• Kubeflow Pipelines - End-to-end ML workflow orchestration
• Ray - Distributed training and hyperparameter tuning
• MLflow - Experiment tracking, model registry, and versioning

Model Serving

• Triton Inference Server - Multi-framework model serving with dynamic batching
• vLLM - High-throughput LLM inference with PagedAttention
• Ollama - Local LLM deployment for on-prem and edge
• KServe - Serverless inference with autoscaling to zero

Observability

• Prometheus + Grafana - GPU metrics dashboards and alerting
• DCGM metrics - GPU utilization, memory, SM occupancy, NVLink throughput
• Custom metrics - Inference latency, throughput, queue depth
• Logging - Centralized log aggregation for training jobs and inference services

Cloud Providers

We work across AWS (EKS), GCP (GKE), and Azure (AKS) with equivalent tooling for each platform.