Raw GPUs to production AI clusters in hours.
Nestor automates the provisioning, configuration, and operation of GPU infrastructure for training and inference.
The problem
Powering GPUs is not the same as running production-ready AI infrastructure.
Installing GPUs is the first step. Before a cluster can run production training or inference, kernel settings, CUDA libraries, inter-node communication, and checkpoint storage must be configured, validated, and maintained as one working system. This work consumes engineering time that your team could spend on your core business.
The solution
One software stack, from raw GPUs to production.
Nestor is the software layer that eliminates this work. It configures, validates, and operates GPU infrastructure for training and inference, so your engineering team saves time.
How it works
Nestor brings kernel settings, drivers, CUDA libraries, networking, and storage into a supported configuration and validates the environment as a working system before handoff.
Nestor tunes each environment to the workload it will run, whether distributed training, LLM inference, or media generation, so clusters are production-ready for the job at hand.
Nestor monitors the cluster, applies updates, and detects and recovers from failures automatically, with capacity from any provider or site managed as one system.
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