Expanse Aims to Unlock GPU Capacity

New YC-backed tool Expanse predicts a workload's true resource needs to increase the effective capacity of expensive GPU clusters.

A new YC-backed startup, Expanse, has launched a tool designed to increase the effective capacity of GPU and high-performance computing (HPC) clusters. The platform integrates with common schedulers like Kubernetes and SLURM to address the problem of resource underutilization. Before a workload is scheduled, Expanse analyzes its source code, job submission script, and the target hardware specifications. By doing this, it predicts the actual computational resources the job will require, rather than relying on user-provided estimates which are often overly generous. The system also aims to flag potential job failures ahead of time, further preventing wasted compute cycles.

This approach directly targets a major pain point for organizations heavily invested in AI and scientific computing: the high cost of idle GPU capacity. GPUs are expensive, and ensuring they are used efficiently is a critical challenge for IT and DevOps teams. By providing more accurate resource predictions, Expanse allows for denser packing of jobs onto clusters, effectively unlocking wasted capacity without purchasing new hardware. This can lead to significant cost savings, faster job completion times, and a more efficient use of existing infrastructure, making it particularly relevant for CTOs and developers managing large-scale compute environments.

The launch highlights a growing industry focus on optimizing computational resources as AI model complexity and data volumes increase. As the demand for specialized hardware like GPUs continues to rise, tools that maximize the efficiency of these expensive assets are becoming essential for managing budgets and scaling operations effectively.