The reliability layer for AI infrastructure.

We work with training labs, inference providers, and neoclouds to benchmark their compute and provide concrete recommendations for improving it.

State-of-the-art measurement

Published GPU specifications generalize across silicon and often overlook details that have to be considered in practice: the silicon lottery, heterogeneous power and networking setups, or cooling. We measure node capabilities in the context of what they're meant to do, providing custom baselines and application-specific fleet optimization. Measurements come from short reference workloads that behave like real training and inference jobs, including synchronized training steps, collective communication, sustained matmul throughput, and checkpoint I/O under load.

We have worked with neoclouds and pre-training companies to understand their compute and optimize their fleets, tailored to their clientele and specific use cases.

Work with us

If you're interested in working with us, reach out. We provide anonymized sample reports from real paid engagements on request.

Self-serve GPU cloud On request Provisioning, GPU health, sustained load, networking, and metering.
Managed Kubernetes and B300 On request Cluster lifecycle, storage, failover, and bare-metal GPU and NVSwitch testing.

Request a sample

Rentals

If you want spot and on-demand instances that have already been through the suite, rent directly through our website.