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Use Cases & Target Industries

Primary Use Cases​

Large-Scale AI Model Training​

Infrastructure for training frontier AI models across hundreds or thousands of GPUs:

  • InfiniBand-connected nodes for efficient multi-node training
  • High-performance parallel filesystem storage
  • GPU collective communication library support
  • Multi-vendor GPU compatibility

LLM Fine-Tuning & RLHF​

  • Dedicated GPU allocation for predictable performance
  • Isolated storage for proprietary datasets
  • Job-level accounting and fair-share scheduling
  • Support for human feedback pipelines on secure infrastructure

AI Inference at Scale​

  • Kubernetes clusters with GPU resource requests
  • Auto-scaling based on demand
  • Load balancing across replicas
  • GPU utilization and latency monitoring
  • Support for model serving frameworks (TensorFlow Serving, Triton, vLLM)

High-Performance Computing (HPC)​

  • Scientific simulation, computational fluid dynamics
  • Molecular dynamics, climate modeling, financial modeling
  • Familiar Slurm tools for batch jobs
  • Job monitoring, interactive sessions, and job accounting

MLOps & Experiment Management​

  • Kubernetes-native workflows for ML pipelines
  • Kubeflow, Airflow, Argo Workflows, MLflow integration
  • Model registry versioning and feature store management
  • Per-project quotas and resource isolation

Target Industries​

Cloud Service Providers​

Multi-tenant isolation, granular metering, billing integration, portal provisioning, support for large-scale GPU deployments.

Sovereign AI Programs​

On-premises deployment, air-gapped support, NIST/ISO 27001/HIPAA alignment.

AI Research Institutions​

Fair-share scheduling, per-researcher tracking, project-based resource sharing.

Enterprise AI Teams​

MLOps workflows, Prometheus/Grafana monitoring, RBAC/SSO/audit logging.

Healthcare & Life Sciences​

HIPAA alignment, tenant-level storage isolation, privacy controls.

Financial Services​

Zero-trust architecture, audit trails, low-latency GPU compute, hardware-enforced boundaries.

Success Metrics​

Use CaseKey Metrics
AI TrainingTime to first run, GPU utilization, checkpoint frequency
LLM Fine-tuningWeekly iterations, model quality metrics
InferenceLatency p99, throughput (requests/sec), GPU utilization
HPCQueue time, cluster utilization, completion rate
MLOpsExperiment velocity, deployment frequency