Our Kubernetes engineers set up what you need at each stage: a GPU cluster, an LLM serving stack, or a Run:ai AI platform. We test each one and hand it over with runbooks.
Book a callToday · GPU servers, each used on its own
Today · A model you want to serve inside the company
Today · Several teams sharing the same GPUs
We install Kubernetes on your servers, automate the GPU drivers, and give your researchers one command to run a job on any free GPU. Your existing servers join the same cluster.
We install llm-d or NVIDIA Dynamo on your GPU nodes, connect it to your gateway and monitoring, and measure it under load.
Cache-aware routing sends each request to the replica that already holds its prompt start.
Prefill and decode run on separate GPU groups that scale on their own.
The cache moves to CPU memory and storage, and one model can span several nodes.
NVIDIA released Run:ai as open source in October 2026. It gives teams quotas, fair-share scheduling, and GPU fractions. It also needs Prometheus, an ingress, TLS, and workload operators. We install all of them, connect them to Run:ai, and customize the platform and the cluster for your teams.
Your GPUs, workloads, teams, and targets.
The open-source tools that fit your environment.
Each tool, connected to your gateway and monitoring.
Tests with your workloads and a written report.
Runbooks, training, and support for your team.
Share each GPU between workloads, overcommit GPU memory, and see what every GPU is doing. Add it to any of the three services.
Tell us about your GPUs and what you want to run. We will suggest the services that fit.