abeebaGPU Infrastructure Optimization
Most GPU fleets run far below capacity while the bill keeps growing. We profile your clusters, find idle and over-provisioned capacity, and re-engineer scheduling and serving so the same hardware does more work for less money.
What's included.
Utilization audit
Per-GPU, per-team and per-model usage profiling that shows exactly where money is spent and wasted.
Serving efficiency
Quantization, batching, KV-cache tuning, speculative decoding and right-sized instances for each model.
Cluster scheduling
Kubernetes GPU scheduling, MIG and time-slicing, queues and priorities so teams share capacity fairly.
Cost governance
Chargeback dashboards, budgets and alerts so finance and engineering see the same numbers.
How the engagement runs.
Measure
Install telemetry and collect two weeks of real utilization, latency and cost data.
Diagnose
Rank savings opportunities by impact and effort, with expected cost and latency effects.
Optimize
Apply the changes in stages behind feature flags, with rollback plans for every step.
Govern
Hand over dashboards, budgets and a quarterly review cadence.
What you keep at the end.
- Utilization and cost baseline report
- Ranked optimization roadmap with savings estimates
- Implemented serving and scheduling changes
- Chargeback and budget dashboards
Tools we commonly use
We pick tools to fit your environment and team. These are the ones we use most often.
How much can we expect to save?
It depends on your starting point. We give you a measured baseline and a savings estimate per change before any work starts, so you decide with real numbers.
Will optimization hurt model quality?
Every change that can affect output, such as quantization, is tested against your evaluation set and only shipped if quality stays within the threshold you set.
Do you work on cloud GPUs as well as owned hardware?
Yes. We optimize AWS, Azure, GCP, specialist GPU clouds and on-prem clusters, including mixed fleets.
Often combined with.
Private & On-Prem LLM Deployment
Run open-weight models inside your own servers, VPC or air-gapped network.
Learn moreCloud-to-AI Migration
Move legacy workloads and data onto AI-ready infrastructure without downtime.
Learn moreMLOps Pipeline Setup
Automate training, evaluation, deployment and monitoring end to end.
Learn moreReady to run AI on your own terms?
Book a 30-minute consultation with an infrastructure engineer. You'll leave with a clear view of options, costs and next steps, whether or not you work with us.