GPU Cluster Orchestration for AI Workloads

AI teams training large models face a brutal infrastructure problem: GPU compute is expensive, scarce, and poorly utilized. Data scientists queue for hours waiting for GPU access on shared clusters, while allocated instances sit idle during data preprocessing or hyperparameter analysis. Spot instance interruptions can destroy multi-day training runs that lack proper checkpointing, wasting thousands of dollars. There is no visibility into cost-per-experiment, making it impossible to compare the ROI of different research directions. Model artifacts are scattered across personal machines and S3 buckets with no versioning or lineage tracking. As organizations scale from single-GPU experiments to distributed multi-node training, the ad hoc tooling that worked for small teams collapses, and researchers spend more time managing infrastructure than advancing their models.

MicrocosmWorks can build an end-to-end GPU orchestration platform that treats compute as a shared, schedulable resource with intelligent queuing, preemption policies, and cost tracking. The platform supports both training and inference workloads with distinct scheduling profiles—training jobs are batch-scheduled across spot and on-demand instances with automatic checkpointing, while inference endpoints auto-scale based on request patterns. A unified model registry tracks every experiment's code, data, hyperparameters, and resulting artifacts with full lineage. Researchers interact through a self-service portal where they define resource requirements and the platform handles placement, scaling, fault tolerance, and cost attribution automatically.

The platform is built over 12-16 weeks in four phases. Weeks 1-3 focus on requirements discovery, GPU workload profiling, and architecture design for the Kubernetes-based scheduling and auto-scaling infrastructure with Karpenter and the NVIDIA GPU Operator. Weeks 4-8 implement the GPU-aware scheduler with bin-packing and gang scheduling, the elastic node pool manager with spot instance bidding strategies, and the MLflow-based model registry with DVC integration. Weeks 9-12 build the self-service researcher portal, cost attribution engine, and per-team budget enforcement dashboards. Weeks 13-16 conduct load testing with representative training jobs, tune checkpoint-and-resume workflows for spot interruptions, and deliver operational training to ML platform and research teams.

• Intelligent GPU Scheduling with Fair-Share Policies: MW can build a custom Kubernetes scheduler that optimizes bin-packing, gang scheduling for distributed training, and priority queues with fair-share policies, maximizing utilization while preventing any single team from monopolizing scarce GPU resources.
• Spot Instance Resilience with Automatic Checkpointing: Rather than simply using spot instances and hoping for the best, MW can implement automatic checkpoint-and-resume workflows that gracefully handle interruptions, capturing 45-60% cost savings without risking multi-day training runs.
• Full Experiment Lineage and Cost Attribution: MW can deliver end-to-end traceability from data version to deployed model via MLflow and DVC, combined with per-job cost attribution that lets leadership compare the ROI of different research directions with real infrastructure spend data.

• Cloud Solutions — GPU cluster provisioning, Kubernetes orchestration, spot instance management, and cost optimization
• AI Development — ML pipeline design, distributed training architecture, model serving, and MLOps best practices

• Hybrid Cloud for Regulated Industries
• Cloud Migration & Cost Optimization
• Serverless Microservices Transformation