# Fault-Tolerant AI Infrastructure Clockwork.io Keeps AI Workloads Running Through Failures By Mursal Rahman | Written with AI assistance | Published 2026-10-05 | Tech Source: Trend Hunter, https://www.trendhunter.com/trends/gpu-resilience-software References: [prnewswire](https://www.prnewswire.com/news-releases/clockworkio-raises-31m-as-linkedin-together-ai-and-whitefiber-adopt-its-resilience-software-to-stop-wasting-gpu-hours-302897588.html)  Clockwork.io is advancing fault-tolerant AI infrastructure with software designed to keep distributed training, inference and reinforcement learning workloads operating through hardware and network failures. Its TorchPass platform can capture snapshots of an entire multi-node training job without requiring changes to training code, allowing work to be restored after major interruptions. Fast asynchronous checkpoints also preserve progress in the background, while LinkPass reroutes traffic around failed network links to prevent unnecessary workload disruptions. The technology addresses the costly GPU downtime and repeated computation that can occur when large AI workloads fail. Clockwork.io can help enterprises and cloud providers increase the amount of [GPU capacity](https://www.trendhunter.com/trends/nvidia1) devoted to productive work while reducing recovery delays. Production adoption by LinkedIn and Together AI also demonstrates opportunities to scale the software across enterprise and [cloud infrastructure environments](https://www.trendhunter.com/trends/cloud-infrastructure-management). Image Credit: Clockwork.io ## Trend Insights (Trend Hunter) - Score: 9.2/10 - Popularity: 75% | Activity: 100% | Freshness: 100% - Audience gender: 50% men, 50% women - Primary generations: Millennial, Gen X - Top markets: North America ## Categories [Trend Hunter](https://www.trendhunter.com/trends) > [Tech](https://www.trendhunter.com/tech) > [AI](https://www.trendhunter.com/ai) ## Key Themes ### Key Themes Behind This Trend - **Resilient AI Workloads:** Fault-tolerant platforms create new value by allowing large-scale training and inference jobs to continue operating through infrastructure failures. - **Asynchronous Checkpointing:** Background snapshot systems reduce wasted computation by preserving AI workload progress without interrupting distributed processing. - **Network-aware AI Recovery:** Traffic rerouting around failed links introduces reliability layers that make GPU clusters more efficient and less vulnerable to downtime. ### Where This Applies - **Cloud Computing:** Cloud providers gain differentiation through infrastructure services that improve GPU utilization and reduce recovery delays for enterprise AI customers. - **Artificial Intelligence:** AI development environments become more scalable when training, inference and reinforcement learning systems can recover from multi-node disruptions. - **Enterprise Software:** Enterprise infrastructure platforms benefit from embedded resilience tools that protect mission-critical AI operations from hardware and network instability. ## Related on Trend Hunter - [Open AI Compute Clouds](https://www.trendhunter.com/trends/Open-AI-compute-clouds.md) - [Durable Agent Execution Standards](https://www.trendhunter.com/trends/agent-executor.md) - [Reliable Workflow Tools](https://www.trendhunter.com/trends/temporal.md) - [Persistent AI Infrastructure](https://www.trendhunter.com/trends/Persistent-AI-infrastructure.md) - [AI Synchronization Hardware](https://www.trendhunter.com/trends/AI-synchronization-hardware.md) - [On‑Prem GPU Clusters](https://www.trendhunter.com/trends/acres-beta-platform.md) - [Distributed Gaming Compute Networks](https://www.trendhunter.com/trends/consumer-gpus.md) - [GPU Development Platforms](https://www.trendhunter.com/trends/rightnow-ai.md)