BenedictENGINEERING / CASE STUDY
Jungle Grid
An execution layer routing AI workloads across heterogeneous compute infrastructure—and an important predecessor to Naught Labs.
Workloads, not machines
Running AI work involves submission, placement, provisioning, monitoring, retries and cleanup across infrastructure with different capabilities and failure behaviour. Jungle Grid addresses the execution lifecycle, not inference alone.
The engineering surface
The workload-oriented layer spans APIs, CLI workflows, provider connectivity, scheduling, monitoring, logs, artifacts and lifecycle management. MCP integration and pre-run estimation belong to that broader AI-workload surface. Specific provider guarantees should be read against the linked project documentation.
Routing is an operational problem
CPU versus GPU placement must consider the workload rather than assume every AI job needs the same hardware. Retry boundaries also matter: what can restart safely, which artifacts survive and how the caller learns the outcome.
Why the next question moved lower
Infrastructure orchestration still assumes an application packages work for external execution. Naught carries the placement question below those APIs toward computation boundaries, native interception and host integration.
Scope
This case study makes no adoption, revenue, utilization or comparative performance claim. Naught’s linked experimental archive records individual runs with their own conditions.