GPU Infrastructure Optimization
Most GPU fleets run far below capacity while the bill keeps climbing. We measure where your compute actually goes, then reclaim it through batching, quantization, scheduling and smarter hardware choices.
Why teams bring us in
GPU cost problems rarely come from one big mistake. They come from idle reservations, oversized models serving simple requests, single-request inference with no batching, and teams who can't see their own spend. Each is fixable, and together they are usually the largest line item in an AI budget.
We begin with two weeks of measurement. Using DCGM exporters, Kubernetes metrics and your cloud billing data, we build a picture of how every GPU is used hour by hour: which team, which model, how many tokens, how much of the card is actually busy. Most organizations are surprised by the gap between reserved capacity and useful work.
- Utilization audit: per-node, per-team and per-model usage with a clear waste breakdown
- Inference tuning: continuous batching, KV-cache sizing, speculative decoding and quantization
- Right-sizing: matching each workload to the cheapest GPU that meets its latency target
- Scheduling: bin-packing, spot/preemptible capacity and fair-share queues for training
- Cost visibility: dashboards that show $/1M tokens and $/training-run by team
Teams that get the most from this service
AI-first companies
Inference cost is part of your cost of goods sold and it is growing faster than revenue.
Enterprises with GPU clusters
Research and product teams share a cluster and nobody can say who is using what.
Teams on reserved cloud capacity
You committed to GPU reservations and want to be sure you are getting value from every hour.
Four phases, each with a sign-off.
The fixes are ranked by dollars saved per week of effort. Typical high-value changes include turning on continuous batching, moving from FP16 to FP8 or 4-bit weights where quality allows, routing simple requests to smaller models, consolidating under-used nodes and shifting batch training to spot capacity. Each change ships with a before/after benchmark so savings are measured, not estimated.
| Phase | What happens | You receive |
|---|---|---|
| Measure | Telemetry from DCGM, Prometheus and cloud billing | Utilization + cost baseline |
| Diagnose | Rank waste by dollars, effort and risk | Prioritized savings plan |
| Optimize | Implement top items with your team | Before/after benchmark report |
| Govern | Budgets, alerts, chargeback | Live FinOps dashboard |
What you keep when we're done.
Savings plan
Every recommendation priced in dollars per month, with effort and risk next to it.
Benchmark report
Throughput, p95 latency and cost per 1M tokens before and after each change.
FinOps dashboard
Spend by team, model and cluster so savings stay saved.
Tools we use
Chosen per client. We work with what your team already runs wherever it makes sense.
Questions about GPU Infrastructure Optimization
Will quantization hurt quality?
Sometimes slightly, often not measurably. We run your evaluation set at each precision and only ship a change when quality stays within the tolerance you set.
Do we need to change our applications?
Usually not. Most optimizations happen in the serving layer. Model routing may need a small change in how requests are tagged, which we implement with your team.
How are savings measured?
Against the baseline we record in the first two weeks: cost per million tokens, cost per training run, and utilization per GPU. The final report shows before and after for every change.
Can you work with our cloud provider's GPUs?
Yes. We work across AWS, Azure, Google Cloud, Oracle Cloud and specialist GPU clouds, as well as on-premises clusters.
Often paired with
Private & On-Prem LLM Deployment
Run open-weight models on your own servers or private cloud, with no data leaving your network.
Explore service →SVC-03Cloud-to-AI Migration
Move legacy workloads and data platforms onto infrastructure that is ready for AI.
Explore service →SVC-04MLOps Pipeline Setup
Automate training, evaluation, deployment and monitoring so models ship like software.
Explore service →Talk to an engineer about GPU Infrastructure Optimization.
A 45-minute call with an engineer, not a sales script. You'll leave with a rough architecture, a cost range and the next three steps.