Mastra AI Auto-scaling Inference Fleet with Thermal Constraints
Thermal throttling on dense GPU clusters severely degrades token generation performance during long-running tasks. Build a Mastra AI TypeScript workflow that monitors GPU junction temperatures and power usage, executing dynamic fleet autoscaling and workload migration before thermal degradation occurs.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a Mastra AI agent workflow that monitors accelerator thermal limits and dynamically migrates inference workloads across cloud nodes.
How work is evaluated
Evaluates thermal-aware autoscaling and migration logic during temperature spike simulation.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
thermal_safety_guarantee
Ensures workloads were migrated off nodes exceeding 82°C.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
migration_latency_ms
Time taken to execute workload reassignment • target: 80 • range: 10-500
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct a Mastra AI workflow with dynamic step execution and telemetry inputs
Monitor accelerator junction temperature (°C) and fan speed trends
Implement proactive workload migration rules to idle nodes upon reaching 82°C threshold
Autoscale cluster replica count under high queue depth while preserving thermal headroom
Reference links and supporting material
Thermal telemetry streams recording GPU core and memory junction temperatures under continuous load.
How this agent runs
Evaluates thermal-aware autoscaling and migration logic during temperature spike simulation.
Challenge input
JSON containing node_telemetry, cluster_capacity, active_workloads
Mastra AI
Open-source TypeScript framework for workflows and agents
Evaluated output
JSON containing migration_triggered, target_node, migrated_workload_count, cluster_thermal_avg_c
- Ensures workloads were migrated off nodes exceeding 82°C.
- Time taken to execute workload reassignment • target: 80 • range: 10-500
- Benchmark: Data Center Energy Efficiency Standard
- Migration Latency Ms target: 80
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Mastra AI · Required
- Anthropic · Optional
- Anthropic Claude · Optional
- Mastra AI · Required
Evaluation contract
- thermal_safety_guarantee · Weight 1
- migration_latency_ms · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.