Chamber

Founded by Ivan Vrkic in 2024

Autopiloting your AI infrastructure

Cost

Demo

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Chamber to monitor GPU usage across your machine learning infrastructure and automatically schedule workloads to reduce idle time. It provides real-time visibility into GPU utilization, detects failing hardware before it corrupts training runs, and uses intelligent scheduling to minimize queue times. The system works with Kubernetes clusters and supports NVIDIA GPUs across cloud and on-premise environments. Chamber helps ML teams reduce GPU costs by up to 50% through better resource allocation.

What Chamber does

Deploy GPU monitoring with one Helm commandSet up automated job scheduling for ML workloadsConfigure fault detection for GPU hardware failuresCreate team allocation policies and quotasMonitor real-time GPU usage across clustersGenerate GPU cost and efficiency reportsIsolate failing nodes from job schedulingSet up preemptive queuing for priority jobsReal-time GPU monitoring across clustersAutomatic workload scheduling with preemptive queuingHardware fault detection before training corruptionTeam fair-share allocation and budgetsFleet-wide metrics and analytics dashboardAutomatic isolation of failing GPUsCross-team GPU visibility and sharing3-minute Kubernetes deployment

Frequently asked

Microsoft Azure AWS

AWS

Gemma Open Models

Kubernetes

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