GrydOS
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Reduce GPU load in seconds when energy spikes

GrydOS lets AI clusters safely respond to energy signals, control non-critical workloads, and unlock new value from flexible compute.

Request PilotSee How It Works
grydos-agent/prod-gpu-westexecuting
flow: signal received → reduce non-critical → log response → resume
[signal]flex_event DR-2026-0401 · target −12% cluster power · window 14:00–14:30 UTC
[grydos]ruleset=prod-gpu-west · scope=non-critical only · production jobs excluded
[reduce]throttle job_queue/ml-training-b · −11.4% draw · checkpoint committed
[log]response 4.2s p95 · delta −10.8% vs baseline · audit_id ae7f3c… written
[resume]window_end · restore throughput · queues nominal · SLA verified
Built for GPU clustersDesigned for data centersGrid-aware by design

Every GPU cluster will eventually need to respond to energy constraints. GrydOS makes that possible.

Compute is growing.Energy is not.

Running AI infrastructure today means:

  • You operate GPUs at full load 24/7
  • You have no way to dynamically reduce consumption
  • You cannot react to grid constraints or pricing signals
  • You're leaving flexibility and potential revenue unused

The reality:

  • Energy is becoming a bottleneck for compute
  • Demand response is rising
  • But compute systems are not built to adapt

The control layer between energy and compute

GrydOS sits between energy signals and your infrastructure.

It turns your cluster into a controllable system, without touching critical workloads.

How it works at a system level:

GrydOS:

  • receives signals (or simulates them)
  • applies predefined safe rules
  • adjusts non-critical workloads
  • records everything

No guesswork. No manual intervention.

System path

  1. Energy signal

    DR · price · dispatch · test

  2. GrydOS

    Policy engine

  3. Workload control

    GPUs · jobs · queues

  4. Logs & metrics

    Audit · deltas · exports

Energy Signal → GrydOS → Workload Control → Logs & Metrics

How it works

Simple. Safe. Controlled.

  1. 1

    Connect your environment

    Integrate your cluster, scheduler, or workload system.

  2. 2

    Define safe control rules

    You choose what can be reduced, paused, or shifted.

  3. 3

    Receive or simulate signals

    Test with simulated events or connect to real signals.

  4. 4

    Execute and log

    GrydOS applies actions safely, logs everything, and restores normal state automatically.

Safety is built-in:

  • Critical workloads are never touched
  • You define all control boundaries
  • Every action is logged and reversible

Run a 2-week flexibility pilot on your cluster

We help you test compute flexibility in real conditions.

Request Pilot

During the pilot, we:

  • Identify flexible workloads
  • Configure safe response rules
  • Simulate or trigger reduction events
  • Measure system response
  • Deliver a full report

What you get:

  • Clear view of your flexibility potential
  • Real performance data
  • Actionable insights for future integration

Compute is becoming energy-constrained

  • AI infrastructure demand is exploding
  • Power availability is now a limiting factor
  • Static compute is inefficient
  • Flexibility will become mandatory

Operators who adapt early gain:

  • cost advantages
  • operational control
  • future access to flexibility markets

Let's talk

Interested in running a pilot or exploring integration?

Or contact us directly: oriccini@grydos.com

Company updates: LinkedIn