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VISION

NVIDIA solved this for itself. AMD hasn't — for anyone.

The gap isn't a missing dashboard. It's a missing closed loop.

NVIDIA ECOSYSTEM

Solved, and bought

DCGM for telemetry, Base Command Manager for orchestration, Run:ai for AI-driven scheduling and ops. NVIDIA valued that operational layer enough to acquire Run:ai for roughly $700M.

AMD INSTINCT ECOSYSTEM

Still tribal knowledge

No equivalent. MI300X/MI355X fleets run on borrowed tooling, spreadsheets, and whoever remembers what happened last time.

ADJACENT, BUT NOT THE ANSWER

Every category nearby solves one slice.

CategoryExamplesWhat it covers
Provisioning / LCMTinkerbell, xCAT, MAAS, Bright, RocksBare-metal imaging — not ongoing health.
ObservabilityPrometheus, Grafana, ElasticDashboards — not remediation.
HPC schedulersSlurm, PBS, LSFJob placement — not fleet health.
Fabric telemetryNVIDIA UFMDeep, but NVIDIA/InfiniBand-only.
WHY NOW
The next intelligence explosion will be gated by uptime, not just compute.

MI300X/MI355X deployments are ramping as neoclouds and hyperscalers diversify GPU supply beyond a single vendor. Every fleet that crosses from a few racks to thousands of nodes hits the same wall: manual operations stops scaling long before the hardware does. The window is open now — before an incumbent, or NVIDIA's own tooling, extends sideways into AMD the way the Run:ai acquisition extended NVIDIA's reach.

Every frontier model trains on a fleet that fails in a thousand quiet ways. We built the layer that notices — and fixes it before you do.
GET IN TOUCH

Building or operating a frontier-scale GPU fleet?