Welcome

This is where the ACE team writes about what we're learning while building autonomous cluster efficiency for large-scale AI training.

Expect posts on:

• Sticky SDC detection — how we catch silent data corruption before it poisons a training run.
• Goodput economics — why 20% recovered goodput is worth more than 20% cheaper hardware.
• Cross-cloud arbitrage — matching workload urgency to the cheapest cost-per-FLOP infrastructure across AWS, GCP, and Azure.

Why we started ACE

Modern GPU fleets fail in ways classical infrastructure never did. A single flaky NVLink or a thermally-drifting HBM stack can quietly poison thousands of GPU-hours of training. Reactive rollback is expensive; ACE is built to prevent the fault from cascading in the first place.

{
  "loop": "fast-interception",
  "signal": "sdc.checksum_divergence",
  "action": "quarantine_node",
  "latency_ms": 42
}

More posts soon. Sign up for the alpha to follow along.