Inference and training run on a deep stack: GPUs and drivers, storage, networking, schedulers, runtimes. Much of the cost hides in the seams between those layers: slow starts, slow data paths, fragile operations.
Tensor Rails builds open-source tools and services for those seams. Each project takes on one problem, works with the stack you already run, and is measured on real hardware.
| Project | Area | What it does | Status |
|---|---|---|---|
| warmctl | Runtime | Snapshots a warmed-up vLLM engine and restores it on any matching GPU host in seconds instead of minutes. | beta |
- Runtime: starting, restoring and scaling GPU workloads quickly.
- Storage and data: fast paths for weights, checkpoints, datasets and caches.
- Distribution: moving models and artifacts between hosts, regions and clouds.
- Operations: diagnostics, health checks and day-2 tooling for GPU fleets.
- Open source first. Services are built on the same open tools.
- Standard interfaces: OCI, Kubernetes, stock drivers and engines.
- Numbers come from runs on real hardware, with the GPUs and versions stated.
Tensor Rails is at an early stage and works quietly. More projects are coming; each is released once it is measured and documented.
Maintained by Kirill Bespalov.