Use Cases
Power governance for AI & ML workloads
AI & Machine Learning
Govern power for runs that can't be interrupted
AI training runs last days or weeks. A power action at the wrong moment — an over-aggressive cap, a misapplied throttle, or a scheduler request that violates fleet policy — can derail a run, or leave you unable to prove what happened to GPU power states when it did.
Spark-XC sits above your GPU, scheduler, and facility systems. Mission Control or your scheduler executes the power action; Spark-XC validates it across five independent paths and commits a Power Event Record — proving the action was approved, safe, auditable, and financially real. Mission Control executes. Spark-XC validates.
1 · GPU Telemetry
2 · Workload Context
4 · Policy Gates
5 · Evidence Chain
09:14:01 [OK] Training epoch 142/500 — nominal
09:14:02 [REQ] scheduler requests cap 320W → 220W
09:14:03 [P1] GPU telemetry verified · 87°C, 318W
09:14:03 [P2] workload context: training, SLA preserved
09:14:04 [P3] facility correlated · rack Δ logged
09:14:04 [P4] policy gate: authority confirmed
[HASH] 09:14:04 4a91...7f30 chained
[OK] 09:14:06 action validated — run preserved
09:14:06 [OK] PER committed · independently replayable
09:14:08 [OK] Training epoch 143/500 — nominal
[HASH] 09:14:08 9bc3...d560 chained
<1s
Power action validated
5
Validation paths
100%
Actions → Power Event Records
0
Unprovable actions
Key Scenarios
Where SPARK-XC governs AI power actions
Scenario · Training
Thermal spike mid-epoch
GPU temperature climbs past the threshold during a long run and a protective throttle fires. Spark-XC validates the action across GPU telemetry and policy as it happens, confirms it was authorized and safe, and commits a Power Event Record — so the throttle isn't just executed, it's proven.
Run preserved. Action authorized and safe. Power Event Record committed.
Scenario · Inference
Scheduler over-allocates power budget
An automated scheduler submits a power-limit request that exceeds the fleet policy for inference serving. The policy and approval gate evaluates the request against the ruleset, denies authorization, and commits the rejection as a Power Event Record — the cluster never leaves its power budget.
Action denied. Inference SLAs maintained. Rejection committed with full context.
Scenario · Multi-GPU
Driver crash during distributed training
A CUDA driver process crashes on one node in a multi-GPU cluster. The firmware-persisted power ceiling was set at initialization and holds regardless of driver state. Spark-XC validates that the ceiling stayed in force and records it — so the node's power posture through the crash is auditable, not assumed.
Power ceiling held. Node recovers, and the evidence is on the chain.
Scenario · Post-Incident
Investigating an anomalous training run
A training run produces unexpected results and the team needs to know what happened to GPU power states during the run. Replay the tamper-evident evidence chain: each power action is a Power Event Record, independently replayable, giving a complete timeline that pinpoints the exact moment and cause.
Root cause identified in minutes by replaying the chain. No guesswork.
Validation Paths
Which validation paths matter most for AI teams
Path 1
GPU Telemetry Validation
Every governed power action is checked against live GPU telemetry — temperature, power draw, and utilization — sampled independently of the training process and the CUDA stack. After the action lands, Spark-XC reads back the hardware state and confirms the intended limit was actually applied.
Why it matters for AI
A silent mismatch between requested and applied power can cause subtle instability that only surfaces epochs later. Validating telemetry pre- and post-action catches it at the moment it happens — not when you're debugging a corrupted checkpoint.
Path 2
Workload / Scheduler Context
Spark-XC validates each action against the workload it touches — which job, which tenant, which SLA. It sits above Mission Control, Slurm, Kubernetes, and Run:ai, so a power action carries the scheduler context that explains why it was taken.
Why it matters for AI
In multi-tenant training clusters, the same power action means very different things to different jobs. Tying every action to its workload context is what makes the resulting Power Event Record meaningful instead of just a number.
Path 4
Policy / Approval Gates
Many competing power requests arrive from schedulers, users, and automated systems. Every one must pass the policy and approval gate before it is authorized — and changes made around the governance layer (e.g. a direct
nvidia-smi -pl) are detected, reconciled, and recorded.Why it matters for AI
Without an approval gate, a misconfigured job or runaway scheduler can drive power limits that violate fleet policy. The gate enforces the rules your infrastructure team defines — consistently, for every request, regardless of where it originates.
Path 5
Tamper-Evident Evidence Chain
Every governed action becomes a Power Event Record on an append-only, SHA-256 hash-chained ledger (optional HMAC signing). The complete history of power actions across a run is always available — immutable, independently replayable, and forensically trustworthy.
Why it matters for AI
When a run produces unexpected results, the first question is whether power behavior was normal. Replaying the chain answers it definitively — exactly what happened to GPU power states, when, and why. No log scrubbing, no missing entries.
AI & ML Teams
Govern power across your training infrastructure
Bring a real power action from a training or inference run, and we'll replay its Power Event Record — approved, safe, auditable, and financially real. We're working with a select group of AI infrastructure teams.