compute/docs/roadmap.md

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.