Metrics and Service Level Agreements (SLAs)

Performance Targets: Metrics and Service Level Agreements (SLAs)

Swarm’s Performance Targets establish benchmarks for ensuring optimal performance across training, inference, storage, and network operations. These targets are tied to Service Level Agreements (SLAs) to guarantee reliability and user satisfaction.

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Metric	Target	Measurement	SLA
Training Speed	90% GPU utilization	Monitored real-time during training operations.	99.9% availability for consistent and efficient training.
Inference Latency	100ms	Measured per request for real-time inference tasks.	99.99% availability to ensure low-latency predictions.
Storage IOPS	100,000 IOPS	Monitored continuously for consistent data access.	99.9% availability to ensure rapid read/write operations.
Network Latency	10ms	Measured end-to-end for distributed communications.	99.99% availability to maintain low-latency networking.

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Descriptions

• Training Speed:

  • Optimized GPU utilization ensures efficient model training, reducing time-to-completion.

• Inference Latency:

  • Guarantees real-time responsiveness for AI predictions, critical for user-facing applications.

• Storage IOPS:

  • High input/output operations per second ensure quick access to datasets, checkpoints, and results.

• Network Latency:

  • Low end-to-end delay supports synchronized and fast distributed AI tasks.

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Key Benefits

• Operational Reliability: High SLAs ensure consistent availability and predictable performance for mission-critical workloads.

• Efficiency: Targets like 90% GPU utilization and low latency maximize resource usage and reduce bottlenecks.

• User Satisfaction: Real-time and low-latency metrics ensure a smooth experience for end-users and developers.

• Scalability: Performance targets enable Swarm to accommodate increasing demands without compromising service quality.

These Performance Targets demonstrate Swarm’s commitment to delivering reliable, high-performance AI infrastructure for a wide range of applications.