GPU Cluster Orchestration for AI Workloads

MicrocosmWorks implements workload-aware GPU scheduling that uses MIG (Multi-Instance GPU) partitioning on A100/H100 GPUs to isolate inference workloads in smaller GPU slices while reserving full GPUs or multi-GPU allocations for training jobs, preventing memory fragmentation from mixed workload interference. The orchestrator understands the memory profiles of different workload types and schedules them to maximize GPU utilization without causing out-of-memory failures from fragmented allocations. For clusters running both inference and training, this approach typically achieves 70-85% GPU utilization compared to the 30-40% common in naively scheduled mixed clusters.

MicrocosmWorks typically deploys GPU orchestration using Kubernetes with the NVIDIA GPU Operator and custom scheduling plugins, enhanced with frameworks like Run:ai or Volcano for gang scheduling, fair-share queuing, and fractional GPU allocation that vanilla Kubernetes does not support natively. Standard Kubernetes treats GPUs as opaque integer resources, while our enhanced stack understands GPU topology (NVLink interconnects, PCIe vs NVSwitch), memory capacity, and compute capability to make placement decisions that significantly impact training performance. For large clusters (50+ GPUs), the scheduling intelligence alone can improve effective throughput by 20-40% compared to default Kubernetes GPU scheduling.

MicrocosmWorks implements multi-tier GPU procurement strategies combining on-demand cloud GPUs for burst capacity, reserved instances for baseline steady-state workloads, and spot/preemptible instances for fault-tolerant training jobs with checkpointing — achieving 40-60% cost reduction compared to on-demand-only pricing. The orchestration layer automatically checkpoints training jobs at configurable intervals, enabling graceful preemption recovery when spot instances are reclaimed, and routes time-sensitive inference workloads to reserved capacity for guaranteed availability. For organizations with sustained GPU demand, we also evaluate colocation with owned NVIDIA hardware versus cloud-only approaches, as the break-even point for owned hardware is typically 12-18 months of continuous utilization.

MicrocosmWorks deploys high-bandwidth, low-latency interconnects using InfiniBand (400Gbps NDR) or RoCE v2 (100-400Gbps) fabrics with NCCL-optimized network topology, because distributed training performance is often network-bound rather than compute-bound when gradient synchronization across nodes creates a communication bottleneck. The network architecture includes topology-aware job placement that co-locates distributed training pods on nodes connected through the same network switch (leaf-spine topology awareness) to minimize cross-switch traffic. For cloud deployments, we leverage placement groups and cluster networking options (AWS EFA, GCP GPUDirect-TCPX, Azure InfiniBand) that provide near-bare-metal network performance, with network architecture consulting at $35-$50/hr.

MicrocosmWorks implements namespace-based multi-tenancy with guaranteed minimum GPU quotas per team, burst capacity above quota when the cluster has idle resources, and priority-based preemption policies that ensure high-priority production inference workloads always get resources even during heavy training periods. The platform includes a self-service portal where team leads can submit training jobs, view queue positions, monitor GPU utilization, and manage their team's job priorities without requiring platform engineering intervention. Chargeback reporting tracks GPU-hours consumed by each team and project, enabling finance teams to allocate AI infrastructure costs accurately across business units.