GPU Infrastructure
GPUs are the most expensive line on your infrastructure bill and, at most companies, the worst utilized — fleets running at 15% while teams wait in queue for capacity, because scheduling, sharing, and capacity planning never got engineered. At GPU prices, utilization isn't a nicety; it's the difference between an AI program that's affordable and one that finance kills.
This is platform engineering with higher stakes, and it's squarely our lane: Kubernetes GPU scheduling with time-slicing and MIG partitioning so small workloads stop hoarding whole cards, bin-packing and priority so training and inference share a fleet gracefully, spot and reserved capacity strategies across clouds and neoclouds, and utilization telemetry that turns "we need more GPUs" into a question with a data-backed answer — which is frequently no.
Kubernetes GPU scheduling
Time-slicing, MIG, and topology-aware placement — cards shared efficiently instead of hoarded.
Utilization engineering
Telemetry, bin-packing, and workload consolidation that turn 15% utilization into 70%.
Capacity strategy
Reserved, spot, and neocloud capacity blended for availability at defensible cost.
Inference fleet operations
Autoscaling, health management, and driver/runtime upgrades without service interruption.
Cost allocation
Per-team and per-workload GPU accounting — the chargeback data that ends capacity fights.
Teams say we need more GPUs. Do we?
Measure before buying: most "capacity shortages" are utilization failures — whole cards pinned to notebooks, no sharing, no preemption, zero off-hours usage. Scheduling and partitioning fixes routinely recover half a fleet's capacity, which is a much better invoice than doubling it.
Spot GPUs for AI workloads — sane or reckless?
Sane with checkpointing, reckless without. Training jobs that checkpoint and resume tolerate preemption for 60–80% savings; latency-critical inference needs a reserved baseline with spot burst on top. The architecture decides whether spot is a discount or an outage.
Do we own hardware or rent from the cloud?
Utilization decides. Sustained high-utilization workloads can justify owned or colo capacity at a fraction of on-demand cost; spiky or exploratory workloads can't. We model your real duty cycle across cloud, neocloud, and owned options — the spreadsheet is unglamorous and decisive.
Put GPU Infrastructure to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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