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Clockwork Systems' new funding and TorchSnap feature address the critical bottleneck of fault tolerance in massive GPU clusters, where hardware failures can idle thousands of healthy units for up to 90 minutes during recovery. By enabling nanosecond-accurate telemetry and synchronization between GPUs, this software layer aims to minimize wasted compute cycles and accelerate training/inference workloads without requiring additional raw hardware. This shift from securing raw resources to optimizing cluster efficiency represents a key evolution in the infrastructure stack necessary for scaling current AI models.

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Clockwork.io bags $31M in funding to keep AI inference and training workloads running like … clockwork

SiliconANGLE5 octombrie 20265 min de cititArticolul și perspectiva sunt în engleză.

Clockwork Systems Inc., the data center infrastructure startup that helps to maximize the efficiency of artificial intelligence chip clusters, has raised $31 million in fresh funding and announced the launch of a new feature called TorchSnap that helps to minimize wasted compute. Today’s round was co-led by Seligman Ventures, Wing Ventures and Premji Invest and […] The post Clockwork.io bags $31M in funding to keep AI inference and training workloads running like … clockwork appeared first on SiliconANGLE.

Clockwork.io bags $31M in funding to keep AI inference and training workloads running like … clockwork Clockwork Systems Inc., the data center infrastructure startup that helps to maximize the efficiency of artificial intelligence chip clusters, has raised $31 million in fresh funding and announced the launch of a new feature called TorchSnap that helps to minimize wasted compute. Today’s round was co-led by Seligman Ventures, Wing Ventures and Premji Invest and also saw the return of existing backers New Enterprise Associates and e& Capital. It brings the startup’s total amount raised to date…

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