Bring a private cluster and one real workload.
We will assess the path from hardware acceptance to production model output: what is blocked, what needs to be instrumented, and what it takes to get to a measurable operating outcome.
contact@menemsha.tech
Send cluster context with the cluster size, where it is in the delivery or acceptance process, and the first workload you need to make real.
Technical first conversation
Useful context: accelerator type, scheduler, storage path, model family, data boundary, team ownership, and the business outcome attached to the workload.
We are most useful when there is a real private GPU environment, a model-production goal, and an accountable owner for the outcome.
GPU/accelerator family, partition size, and node shape.
Slurm, Kubernetes, Run:ai, or local admission policy.
Dataset location, throughput path, sharding, and access constraints.
The first model run, fine-tune, eval, or serving path that matters.
Architecture, parameter scale, context needs, and serving shape.
Private, regulated, air-gapped, sovereign, or internal-only constraints.
Person accountable for cluster access, jobs, and operating policy.
Person accountable for the workload outcome and acceptance criteria.