Clockwork FleetIQ Platform

Nano-second Accurate Visibility Correlated Across the Stack

AI at scale slows when GPU, cluster, or cloud communication falters. FleetIQ unifies nanosecond-level visibility, dynamic traffic control, and job-aware resilience in one software control plane — transforming communication into a performance lever. The result: fewer restarts, faster training, true operating capacity.

3 Dysfunctions AI Infrastructure Teams Grapple With

Dysfunctional networks hurt GPU Utilization, Job Completion Time, overall ROI

  • Link flaps
  • NIC failure

Troubleshooting unpredictable queues and slow jobs is hurt by poor visibility into network misconfigurations, link flaps and congestion.

Network links routinely fail or degrade, and a single link flap in a large cluster can cause job restarts, wasting thousands of GPU hours.

Network congestion and contention results in too much time spent on data ingestion and exchange instead of on compute, slowing AI jobs.

AI Fabrics Are Different

LLM Training Patterns vs. Traditional Cloud Computing

  • Separate back-end and front-end networks
  • Highly demanding back-end network:
    • Lossless
    • Very high-bandwidth
    • Low latency and jitter
    • In-order delivery
  • Frequent network failures due to optical port density, overheating, dust, etc.

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A Fabric Built for AI at Scale

Clockwork Frees AI from Communication Constraints

Clockwork FleetIQ transforms AI infrastructure by unifying nanosecond visibility, job-aware resilience, and dynamic traffic control into one software-driven AI fabric. Unlike static, vendor-bound networks, FleetIQ runs anywhere—across GPUs, NICs, switches, and transports—normalizing performance and accelerating training without application changes.

From Precision to Control: End-to-End Fabric Intelligence

Transform AI fabrics into resilient, high-performance networks

Global ClockSync Dynamic Traffic Control

Clockwork FleetIQ Platform Foundation: Global ClockSync

Sub-microsecond accurate visibility

Global ClockSync aligns every host, NIC, and switch to a shared sub-microsecond timeline. This unified clock enables precise telemetry and real-time correlation across jobs, GPUs, and networks—turning invisible slowdowns into observable, actionable data.

Delivers Nanosecond Telemetry, Unified Time Sync and Precise Root-Cause Attribution.

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Clockwork’s NCCL Plugin Provides Granular Fleet & AI Job Visibility

Supports RoCE and InfiniBand

Dynamic Traffic Control (DTC) actively steers flows to avoid collisions and incast collapse. By pacing queue pairs and shifting traffic across underutilized paths, DTC bounds tail latencies and keeps synchronized collectives moving forward.

Delivers Network Auto-failover, Congestion Control and Load Balancing

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Addressing the Visibility Gap: Clockwork Fleet Audit, Fleet Monitoring, Workload Monitoring

From clean starts to continuous uptime: end-to-end AI fleet assurance

Provisioning Operations:

  • Provision Nodes, Network, Storage, Firmware, Base Schedule
  • Observe, detect, troubleshoot, fix and optimize Infrastructure

Keep the fleet healthy, performant and cost-effective while AI jobs run.

Clocksync Foundation

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

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Disruptive Network Failures and Link Flaps Are Common and Expensive

Failures Happen Frequently—Even in Brand New Clusters

  • Time of first job failure in brand new cluster: 26.28 minutes

“Achieving high utilization with them (GPUs) is difficult due to the high failure rate of various components, especially networking.

8-24 engineer hours lost per incident

Workload Acceleration

Proven Throughput Gains Across Real-World AI Workloads

Hyperscaler with Clockwork vs Dynamic Load Balancing (DLB)

2 all-to-all jobs

  • The hyperscaler with Clockwork enabled has 33% more outbound throughput vs. DLB

Large Social Media Company with Clockwork vs. ECMP

2 all-to-all jobs

  • The large social media company with Clockwork enabled has 29% more throughput vs. ECMP

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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.