KTLO-000 · Houston, we have a GPU problem
Every AI lab is running a mixed fleet across accelerator generations and vendors, growing faster than headcount can follow. KTLO AI Labs builds health monitoring, remediation, and human-in-the-loop managed services purpose-built for AI cluster fleets — catching the ECC errors, thermal throttling, and RDMA fabric flaps that quietly burn compute hours, so your uptime and utilization stay high without a bigger on-call team.
AMD Instinct supported today · NVIDIA on the roadmap
Fleet view — 64 nodes
A single GPU, a node, a rack, an entire AI Super Cluster Pod — it's all one machine now.
Traditional monitoring was built to watch one server at a time. That model breaks down the moment your unit of compute — and your unit of failure — spans racks and entire AI Super Cluster Pods of interconnected accelerators acting as a single system. You can't reason about one GPU in isolation anymore, and legacy tooling simply doesn't know how to look at the whole thing.
AI clusters fail quietly, not loudly.
Talk to anyone running a large GPU fleet and the same pain points come up. They shaped everything we're building.
GPU-hours leak silently
A node that throttles, remaps memory pages, or drops a link doesn’t crash — it just runs slow. Every job scheduled onto it pays the tax, and snapshot monitoring misses the slow degradation.
One bad link stalls the whole job
Collective training is only as fast as the slowest rank. A flapping RDMA link or a hung ring can stall a run for everyone, with no clear signal pointing at which node caused it.
Monitoring is vendor-locked
Tooling built for one accelerator vendor doesn’t map to another. A mixed or migrating fleet ends up with two stacks, two vocabularies, and two on-call playbooks.
Alerts arrive without answers
A page with a metric and no context wakes someone up who then has to go hunt for the runbook — costing precious minutes while the fleet keeps degrading.
One loop, running across every node in the fleet.
Software catches the fault, classifies it, and can act on the node — and where a problem needs hands, our managed service puts a person on it. Same vocabulary, end to end.
Detect
Continuously watch compute, fabric, and storage for the signals that precede failure.
Classify
Turn every anomaly into a stable, vendor-neutral fault code with a clear severity.
Alert
Route the fault to the tools your team already runs, with the fix already attached.
Remediate
Act on the node safely — guarded by dry-runs, cooldowns, and concurrency limits.
Escalate to humans
When hands are needed on the floor, our managed service puts the right person on it.
We're building the platform in the open.
KTLO is being built with early design partners running real GPU fleets. It's not generally available yet — join the waitlist and we'll bring you in as we open up early access, starting with AMD Instinct clusters on Kubernetes.
No spam. Just early access, launch updates, and the occasional fault code we're proud of catching.