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Denpex is designed for the operators who diagnose the failure, the engineers whose jobs are blocked by it, and the infrastructure leaders accountable for recovered GPU time and safe remediation.
Denpex is built for teams running distributed GPU training at scale. A slow diagnosis costs real GPU hours.
Training infrastructure engineers, MLOps engineers, SREs and platform owners who need one evidence path across framework, scheduler, network and GPU failures.
Multi-tenant infrastructure where a hardware fault must be separated from workload and fabric causes before draining a node or opening an RMA. Maximum supported GPU cluster size: 16,384 GPUs; independent fabric validation is still required before that figure is described as validated.
Engineers operating vLLM, TensorRT-LLM, distributed training and long-running research workloads where a failed rank or unhealthy worker can waste expensive capacity.
Universities, national laboratories, enterprise AI, autonomous systems, defense teams, OEMs and integrators that need Slurm-aware evidence, controlled data movement and reviewable remediation.
Denpex does not currently publish a named customer reference or permissioned customer outcome case study. The evaluation protocol defines how a pilot measures incident time saved, false RMA avoidance, recovered GPU time and support-ticket deflection without presenting modeled savings as customer results.
Scope a measurable pilot