A Slurm-orchestrated HPC cluster, GPU-ready inference nodes, and a local model stack that runs open-source and enterprise models on demand — CPU and GPU resources provisioned as workloads need them.
A dedicated DDR4 ECC HPC cluster, GPU inference workstation, an enterprise-API inference node, and a fleet of engineering workstations — all owned and operated locally.
Every job flows through the Slurm workload manager, which schedules across CPU partitions and allocates GPU resources on demand via generic resource (GRES) scheduling.
Resources are allocated per workload and released back to the pool when done — no idle capacity, no lock-in.
Open-source models run locally on the cluster; enterprise models are reached through the dedicated inference gateway — one stack, both worlds.
The same battle-tested bioinformatics and MLOps stack SyncBio runs in production — now backed by dedicated local compute.
The infrastructure is already built, running and growing in Puducherry — available to partners, government and research today.