GPU HARDWARE INVENTORY TRACKING
GPU asset tracking must capture per-device information: GPU UUID, PCI bus address, memory size, firmware version, manufacturing date, power cable serial numbers, and cooling loop connections. Each metric affects replacement logistics. A cluster of 1,024 H100 GPUs across 128 nodes requires tracking 1,024 power cables, 128 InfiniBand switch ports, and 256 cooling line connections.
Automated discovery via NVIDIA DCGM and IPMI detects 99.8 percent of hardware assets without manual inventory. Firmware compliance tracking ensures all GPUs run the same VBIOS version. Version drift across a cluster increases support issues by 35 percent. Automated firmware updates during maintenance windows reduce drift to zero.
| Asset Attribute | Discovery Method | Update Frequency | Impact if Stale |
|---|---|---|---|
| GPU UUID | DCGM GPU inventory | Real-time | Warranty claims rejected |
| Firmware version | nvidia-smi -q | Daily | 35% more support issues |
| Memory health | DCGM diagnostics | Hourly | Silent ECC degradation |
| Power cable ID | Manual RFID scan | Quarterly | 48-hour replacement delay |
| Cooling loop ID | Manual BMS integration | Monthly | Overheating risk |
| Warranty expiry | Procurement database | Monthly | $12,000+/repair out-of-warranty |
UTILIZATION TRACKING AND ANALYSIS
GPU utilization is not a single metric. GPU compute utilization measures streaming multiprocessor activity, GPU memory utilization measures HBM usage, and GPU power utilization measures actual power draw. A GPU may show 95 percent compute utilization while memory utilization is only 40 percent, indicating memory-bound workload. Combined utilization score weights all three equally for capacity planning.
Allocated vs utilized time differs significantly. In a typical 1,000-GPU cluster, 28 percent of allocated GPU-hours are idle (job setup, teardown, between jobs). Node-level utilization shows 65 percent average, with 45 percent during non-peak hours and 85 percent during peak. Right-sizing allocations and improving scheduler efficiency can reclaim 15-20 percent of effective capacity.
| Utilization Metric | Typical Cluster Average | Target | Measurement Tool | Optimization Lever |
|---|---|---|---|---|
| GPU compute util | 65% | 75-85% | DCGM / nvidia-smi | Better scheduling + right-sizing |
| GPU memory util | 55% | 70-80% | DCGM | Memory-aware scheduling |
| GPU power util | 50% | 60-75% | PDU / DCGM | Power capping + DVFS |
| Allocated time util | 72% | 85-90% | SLURM accounting | Preemptible partitions |
| Job efficiency | 85% | 90-95% | Profiling tools | Kernel optimization |
GPU LIFECYCLE MANAGEMENT
GPU hardware follows a 3-5 year lifecycle. Year 1-2: peak performance with full warranty. Year 3: performance degradation of 3-8 percent from thermal cycling. Year 4: failure rate increases to 4-6 percent annually. Year 5: end-of-warranty with 8-12 percent annual failure rate. GPU fan replacement at year 3 costs $200 per unit and reduces thermal-related failures by 60 percent.
Depreciation schedules for GPU assets follow 3-year MACRS. A $30,000 H100 depreciates $10,000 annually. Residual value after 3 years is $6,000-$9,000 (20-30 percent). Lease vs buy analysis at $3.50/GPU-hour on-demand shows breakeven at 18 months for reserved instances and 30 months for dedicated clusters.
COST ALLOCATION AND CHARGEBACK
GPU asset cost allocation requires per-GPU-hour costing including hardware depreciation, power, cooling, networking, and facilities overhead. Standard allocation: GPU hardware 62 percent, power 18 percent, cooling 8 percent, networking 7 percent, facilities 5 percent. Per-GPU-hour cost for H100 ranges from $2.10 (3-year reserved, 85 percent utilization) to $5.80 (on-demand, 50 percent utilization).
Showback vs chargeback models affect GPU utilization behavior. Showback (informational reporting without billing) results in 15-20 percent lower utilization than chargeback (actual billing to team budgets). Chargeback with market-based pricing incentivizes efficient GPU usage but requires 3-6 months for proper budget allocation cycles.