AI & LLM Infrastructure
infraOk AI & LLM Infrastructure builds and runs the platform under your models: GPU clusters on Kubernetes, reproducible training and fine-tuning pipelines, experiment tracking, and inference serving that keeps its latency when traffic spikes.
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Sound familiar?
“GPUs sit idle overnight while the team waits in a queue during the day.”
“A fine-tuning run died at hour 19 and there was no checkpoint to resume from.”
“The model works in a notebook, and nobody knows how to serve it at 200 requests a second.”
“Inference cost per request is a number nobody has measured yet.”
What we take on
| GPU clusters | Kubernetes with the NVIDIA GPU Operator, node pools sized separately for training and inference, and Kueue so jobs queue fairly. |
|---|---|
| Training pipelines | Distributed training and fine-tuning with PyTorch and Ray, regular checkpoints to object storage, and runs you can reproduce from a commit. |
| Experiments and registry | MLflow or Weights & Biases for experiments, and a model registry that records what went to production and when. |
| Inference serving | vLLM, KServe or NVIDIA Triton behind autoscaling that watches GPU utilization and queue depth. |
| Data pipelines | Batch and streaming pipelines that feed training and keep features consistent between training and serving. |
| GPU cost | Spot GPUs for training that can resume, reserved capacity for serving, and a cost per request you can put in a pricing model. |
What you get
- A training pipeline that goes from a dataset version to an evaluated model in one command
- Serving endpoints with latency and throughput dashboards
- GPU utilization and cost reports by team and project
- Runbooks for the failures that actually happen: a GPU out of memory, a stuck NCCL job, a model that won't load
How we work
- Week 0–1
Assess
Architecture, security, reliability and spend review, delivered as a prioritized report.
- Week 1–2
Plan
Scope, SLAs, runbooks and success metrics agreed in writing before anything changes.
- Week 2–4
Build & onboard
Tooling, access and infrastructure as code in place, then a supervised handover to operations.
- Ongoing
Operate & optimize
24×7 operations, monthly service reviews and continuous cost and performance tuning.
Tools and platforms
Compute
- NVIDIA GPU Operator
- Kueue
- Karpenter
Training
- PyTorch
- Ray
- Kubeflow Training Operator
Tracking
- MLflow
- Weights & Biases
Serving
- vLLM
- KServe
- NVIDIA Triton Inference Server
Data
- Apache Spark
- Apache Kafka
- Feast
What we don't do
- Designing your models or choosing your training data. Your ML team owns the science; we own the platform it runs on.
- Labeling data.
- Promising GPU availability from a cloud provider. We plan around shortages and help you reserve capacity early.
Who this helps
Company types
Industries
Questions, answered
Which model serving frameworks does infraOk support?
infraOk deploys vLLM, KServe and NVIDIA Triton Inference Server, and chooses between them based on your models, latency targets and traffic patterns.
Can infraOk run fine-tuning on our own data securely?
Yes. infraOk runs fine-tuning inside your cloud accounts, limits data access to the training jobs that need it, and records which data version produced each model.
Does infraOk build the models?
No. Your ML team owns the models and the data science. infraOk builds and runs the platform they train and serve on.
Get a free cloud assessment
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