Clockwork.io

Stand: Booth 821
Clockwork.io pioneers Software-Driven AI Fabrics™ - a programmable layer between hardware and workload that delivers nanosecond-accurate telemetry, AI fault tolerance, and performance optimization across any accelerator, network, or deployment model. Modern AI workloads need the whole cluster to act as one machine, but failures and infrastructure bottlenecks severely compromise efficiency. Clockwork.io's FleetIQ platform recovers that lost capacity, letting enterprises train, deploy, and serve the world's most demanding AI workloads faster, more reliably, and at lower cost - across any Ethernet, RoCE, or InfiniBand fabric, without hardware lock-in. TorchPass, Clockwork.io's AI fault tolerance product, is independently benchmarked by SemiAnalysis as the only solution that maintains full training throughput during failures, outperforming checkpoint-restart and leading open-source frameworks. Uber, Wells Fargo, DCAI, Nebius, NScale, and White Fiber trust Clockwork.io to power their AI infrastructure. Learn more at www.clockwork.io
 
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Sponsor Editorial

Data Center Networking in 2026 - Scaling to New Heights: Up, Out, and Across

Clockwork.io
AI workloads are fueling a massive expansion of data center infrastructure. Leading technology companies are projected to spend over $630 billion on AI infrastructure in 2026. A key component of this …
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The AI infrastructure race is measuring the wrong thing

Clockwork.io
The AI industry keeps counting GPUs and gigawatts, but the real competitive edge is shifting toward squeezing more useful work out of the infrastructure companies already own — not buying more of it.
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Clockwork Fleet-Monitoring: A Layered Architecture for GPU Network Observability

Clockwork.io
Why large-scale GPU fabrics fail partially and silently — links stay "up" while training slows down — and how combining edge-based one-way-delay probing with switch telemetry closes the gaps neither c …
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TorchPass Live GPU Migration for Resilient Distributed Training

Clockwork.io
Eliminate the biggest source of wasted GPU-hours in distributed training. Instead of restarting from the last checkpoint after a failure, TorchPass migrates training state live to a spare GPU and resu …
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