# Lumbridge Compute roadmap

Lumbridge Compute is the resident AI workload control plane for unified-memory machines.

## Core contracts

1. **Models** — local vetted launch recipes with immutable revisions and measured footprints.
2. **Scenes** — named workload sets activated through admission control.
3. **Evals** — versioned datasets, prompts, harnesses, assertions, and portable results.
4. **Artifacts** — resumable weights, adapters, checkpoints, datasets, and result bundles.
5. **Jobs** — inference, evaluation, download, conversion, and artifact-adoption lifecycles.

Training execution is deliberately outside this control plane. Forge records the handoff to
Prime-RL and the resulting provenance; Prime Intellect owns rental infrastructure. Compute
admits and serves an approved artifact after it has been returned, hashed, and registered. It
does not implement another trainer or become a marketplace client.

## Near term

- Resumable, checksum-verified `lumbridge-compute model pull` with leases and progress state.
- Eval comparison, regression thresholds, warmup policy, and concurrency/load tests.
- Extend the shipped server-native Prometheus ingestion and launch-configuration
  history with TTFT/latency percentile rendering and retention compaction.
- Scene hooks for preflight, post-activation eval gates, rollback, and schedules.
- Gateway aliases so clients follow the active scene without configuration changes.
- A fail-closed artifact adoption command for checkpoints returned by an approved Forge run:
  verify digest and metadata, measure the local footprint, then promote into the registry.
- Checkpoint discovery, resumable transfer, retention, and rollback after registry promotion.
- A four-lane fair-share Jobs scheduler driven by observed queue pressure and
  latency: three background lanes, one interactive reserve, and safe C4 borrowing.

## Open-source readiness

- Keep public scene/eval manifests command-free; commands remain in the trusted local registry.
- Add CI across x86_64 and aarch64, unit/integration tests, security policy,
  contribution guide, code of conduct, changelog, and versioned JSON schemas.
- Remove private hostnames, voices, paths, and finance datasets from public fixtures;
  ship generic examples and keep personal overlays outside the repository.