PROOF INPRODUCTION.

Challenge

A well-funded AI research lab needed to scale from a 64-GPU pilot to a 512-GPU production training cluster in under 90 days — without disrupting ongoing research runs.

Solution

We designed a 4-rack, 512x H100 SXM5 cluster with 400G InfiniBand HDR fabric, NVMe-oF shared storage at 40TB/s aggregate bandwidth, and a custom job scheduler integration. Delivered on day 87.

Key Specs

Challenge

A cloud provider needed a custom inference accelerator that could be deployed at hyperscale — with specific power envelope, PCIe form factor, and latency targets that no off-the-shelf card could meet.

Solution

Full-stack engagement: FPGA-based accelerator design, custom firmware, PCIe driver development, and integration with the provider's existing orchestration layer. 18-month program from spec to production.

Key Specs

Challenge

An ML platform company was bottlenecked on data ingestion — their training jobs were GPU-idle 35% of the time waiting on storage I/O. They needed a storage architecture that could keep up with 256 GPUs.

Solution

Redesigned storage fabric using NVMe-oF over RoCEv2, deployed a Lustre parallel filesystem with custom tuning for ML workload access patterns, and implemented a tiered caching layer for hot dataset reuse.

Key Specs