Turn findings into a working production foundation.
A scoped implementation phase that resolves the critical blockers and establishes the foundations for sustained training runs and a baseline inference endpoint.
Known blockers prevent reliable training or baseline inference.
Blocker register tied to launch recipes, dashboards, and owners.
Foundation ready for a priority workload or remaining gaps explicit.
AI platform owner with infrastructure and workload access.
The work inside Production Enablement.
Communication backend
Tune NCCL/RCCL settings, process placement, rank mapping, and topology constraints for the accelerator and network fabric in front of us.
Data pipeline
Implement or repair the data path so training steps are compute-bound more often than I/O-bound, with measured throughput and failure visibility.
Parallelism templates
Validate data, tensor, pipeline, sequence, and optimizer-sharding patterns against the target model class and cluster topology.
Observability and recovery
Instrument GPU, network, storage, scheduler, job, checkpoint, and model metrics so failures are debugged from evidence, not memory.
Baseline serving stack
Deploy a first serving path with batching, KV-cache, quantization direction, SLO dashboards, and cost-per-token instrumentation.
Runbook handoff
Package launch recipes, escalation paths, dashboards, acceptance checks, and rollback/recovery steps for the client team.
- Tuned cluster-level configuration and launch recipes
- Validated training template for the target model family
- Working inference endpoint with SLO telemetry
- Observability dashboards and alerting baseline
- Checkpoint/restart and incident runbooks
- Production readiness review with remaining risks