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Keep training jobs alive and models moving to market faster

  • Clockwork ensures your AI jobs don’t crash when infrastructure falters.
  • Nano-second precise monitoring, auto-remediation and sub-microsecond stateful job failover combine to keep workloads progressing without costly restarts.

The result: higher model uptime, faster time-to-train, and quicker paths to production

Maximize AI Model Uptime

See Clockwork In Action

Prevent AI Job Interruptions

Preempt interruptions before they stall progress

Minimize checkpoints. Maximize uptime. Accelerate results. Traditional clusters restart more, checkpoint more, and deliver less. With Clockwork, preemptive fault tolerance keeps training and inference jobs running without constant interruptions. Reduce restarts, cut wasted checkpoint time from hours to minutes, and accelerate time-to-market for large models.

Status Quo: More AI Job Restarts More Checkpoints Slower Time-to-Customer Value Multiple Checkpoints 
 60 Minutes of Checkpoint Time = 
Delayed Job Completion Time 6 checkpoints 10 min 10 min 10 min 10 min 10 min 10 min With Clockwork: Preemptive Reduction of Checkpoints Minimize AI Job Restarts
Minimize Checkpoints
Accelerate Time-to-Customer Value 10 Minutes
 of Checkpoint Time = 
Fast Job Completion Time 1 checkpoint 10 min
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Stop Wasting GPU Cycles
On Network Communication Inefficiency

Turn idle communication time into productive compute.

Source: AMD


 40% Network communication time where GPU is idle 
 60% GPU utilization Compute Efficiency

Up to 40% of cluster time can be lost to communication bottlenecks — leaving GPUs idle while workloads wait. Intelligent traffic routing keeps compute flowing, boosting utilization, throughput, and ROI from every GPU hour.

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Close the AI Efficiency Gap By Maximizing GPU Utilization

Identify cluster inefficiencies in real-time

Pinpoint inefficiencies with nanosecond precision. Fix them in real time.

NIC failure Link failure Link failure NIC failure Slow, error-prone manual fixes Real-time auto-remediation Auto-remediate inefficiencies in real-time

Crash-Proof AI Jobs.

Trace. Correlate. Optimize — across every layer

Supercharge cluster utilization and customer innovation

  • Avoid job crashes from GPUs falling off the bus to NIC failures or timeouts from link flaps or dust particles

  • Dramatically reduce checkpoint recoveries

  • Accelerate experiments and time-to-market

NIC failures GPUs falling off the bus Link flapping SLURM_ID ranking NCCL_test profile Misconfigurations Cabling Switches Software NCCL deadlocking/stalling RCCL_test profile Reasons for Cluster Inefficiency 
 Rapid Fault Isolation with sub-microsecond 
cross-stack visibility with stateful fault tolerance Crash-proof AI Jobs 
 reroute traffic in real-timewith dynamic traffic control Maximize Cluster Utilization 
 AI Training & Inferencing Global 
Clock Sync Dynamic 
Traffic Control CPU CPU App GPU GPU CPU CPU App GPU GPU CPU CPU App GPU GPU Compute Cluster Storage AI Factories Observe Monitor Trace Correlate End-to-end Visibility

From NIC failures to GPU drop-offs, Clockwork detects issues in nanoseconds and re-routes workloads in real time. Dynamic traffic control ensures uninterrupted performance — enabling stateful fault tolerance that keeps massive AI jobs running non-stop.

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Bulletproof Jobs With One Pane of Glass.

Integrate Clockwork cross-stack metrics into Grafana Dashboards

Clockwork provides fine-grained, cross-stack observability with API-level access to integrate with your existing Grafana dashboards. Monitor workloads, correlate performance with fabric metrics, and trace bottlenecks with nanosecond precision. Finally, one pane of glass for compute, storage, and networking.

Clockwork UX Granular Cross-stack Metrics Clockwork API For Cross-stack Metrics

Single Pane of Glass Grafana Dashboards

Accelerate Time-to-Value. Every Time. At Any Scale.

Deliver consistent performance from fleet audit to runtime.

Clockwork validates and audits fleets on Day 1, then proactively remediates runtime issues with sub-microsecond response. Continuous performance monitoring ensures workloads maintain consistent SLA targets while cutting operational overhead. Faster deployments. More resilient fleets. Lower costs. Communicate

Provisioning 
 Proactive 
configuration checks Operations Sub-microsecond runtime remediation Provision Nodes, Network, Storage, Firmware, Base Schedule Observe, detect, troubleshoot, fix and optimize Infrastructure

Deploy a consistent & reproducible good baseline fleet to run AI workloads

Run continuosly performant AI workloads with stateful fault tolerance and a more cost-effective fleet operations

Fleet Audit

  • Software checks
  • Node checks
  • Front-end network
  • Back-end GPU 
network validation

Fleet Monitoring

  • Runtime link failures/flaps
  • Runtime fabric topology
  • Runtime fabric performance
  • Congestion and 
contention monitoring

Workload Monitoring

  • Deep workload visibility
  • Correlation of data path performance with network metrics to identify root cause of job performance

Deliver Faster AI Jobs, Better Cluster Up-time and Hardware Independence

Maximize cluster utilization 
with more concurrent AI jobs

Clockwork eliminates AI job contention with advanced flow control, enabling more concurrent jobs, higher utilization, and greater uptime. Higher utilization means lower compute costs and faster training cycles. Clockwork achieves this by classifying flows at the source, using sub-microsecond telemetry to steer around hot paths, pacing background traffic, and admitting or deferring work to protect priority jobs.

Software-Driven Fabric creates a unified software control plane

Clockwork’s Software-Driven Fabric (SDF) is the architectural foundation of the solution — an intelligent, nanosecond precise unified software control plane that statefully orchestrates all traffic, sustains high cluster utilization, accelerates time-to-train, and makes large AI fleets economically scalable.

Increase customer choice and investment protection with 
100% Software Driven Fabrics

AI factories are inherently heterogeneous — with diverse GPUs, NICs, switches, transports, and APIs. Vendor-specific optimizations may deliver short-term gains, but they create brittle fabrics and lock customers into costly refresh cycles.

Clockwork’s Software-Driven Fabric (SDF) provides a unified software control plane: any GPU, any NIC, any switch, any transport. By normalizing vendor and generational differences, SDF delivers consistent visibility, real-time control, and fault tolerance across InfiniBand, Ethernet, and RoCE — without application changes.

The result: true hardware independence, faster adoption of new technologies, and protection of past investments. With Clockwork, every GPU hour counts toward customer value.

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Stop wasting GPU cycles. Start scaling smarter.
Clusters must deliver high uptime while running at maximum efficiency.

Turn your GPU clusters into a competitive advantage—not a cost center.

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