Chamber (YC W26), an AI-powered infrastructure management tool for GPU clusters, automates tasks like workload orchestration and failure diagnostics. Built by ex-Amazon platform engineers, it addresses the pain point of ML teams spending excessive time maintaining GPU fleets instead of innovating. The tool integrates with Kubernetes, Slurm, and multi-cloud environments, offering real-time monitoring, automated root-cause analysis, and graduated autonomy for safety-critical operations.

Chamber’s agent operates within existing workflows, handling routine tasks such as resubmitting failed jobs with corrected resource configurations or isolating faulty nodes. It maintains a live model of GPU fleets, enabling actions like adjusting resource allocations or provisioning infrastructure through validated, rollback-safe operations. Safety is prioritized: any action affecting other teams’ workloads or production jobs requires human approval. The platform’s diagnostic capabilities correlate GPU metrics, workload history, and cluster topology to pinpoint failures—e.g., distinguishing between a generic “OOM” error and one caused by VRAM exhaustion.

Early adopters include AI research teams, with Chamber deployed in hybrid setups across AWS, GCP, and on-prem clusters. Pricing is under evaluation, with models like per-GPU-under-management and tiered plans being tested. A demo of Chambie, the interactive AIOps teammate, shows workload statuses, costs, and GPU utilization, highlighting its practical impact. One queued job for a multimodal project shows a wait time of ~4 minutes, while a failed RLHF training run is automatically logged with contextual insights.

The tool aims to reduce “infra babysitting” by up to 50%, freeing engineers to focus on ML innovation. Chamber’s founders invite feedback from teams managing GPU clusters, emphasizing iterative improvements based on user needs. As one user noted, “Finally, a system that understands GPU infrastructure as deeply as we do.”