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Compute

The Compute section manages the computational resources that power your AI operations, providing control over performance, scaling, and resource allocation.

Note: The Mamentis API has not been published yet; programmatic access is coming soon.

Compute Resources​

CPU Resources​

  • Standard CPUs: General-purpose computing power
  • High-Performance CPUs: Optimized for complex calculations
  • ARM Processors: Energy-efficient computing
  • Custom Configurations: Tailored to specific workloads

GPU Resources​

  • NVIDIA A100: High-end training and inference
  • NVIDIA V100: Versatile AI acceleration
  • NVIDIA T4: Cost-effective inference
  • AMD MI250: Alternative GPU options

Memory Configuration​

  • RAM Allocation: System memory for model loading
  • VRAM: GPU memory for model operations
  • Storage: Fast SSD storage for model files
  • Cache: High-speed temporary storage

Specialized Hardware​

  • TPUs: Google's Tensor Processing Units
  • FPGAs: Field-programmable gate arrays
  • Neural Chips: Purpose-built AI processors
  • Quantum: Experimental quantum computing

Resource Management​

Load Balancing​

Distribute workloads across resources:

  • Round Robin: Even distribution across instances
  • Least Connections: Route to least busy instance
  • Resource-Based: Consider CPU/memory usage
  • Geographic: Route based on user location

Performance Optimization​

Model Optimization​

  • Quantization: Reduce model precision for speed
  • Pruning: Remove unnecessary model parameters
  • Distillation: Create smaller, faster models
  • Caching: Store frequently used model outputs

Inference Optimization​

  • Batch Processing: Group multiple requests
  • Pipeline Parallelism: Split models across devices
  • Model Sharding: Distribute large models
  • Dynamic Batching: Optimize batch sizes automatically

Memory Management​

  • Model Offloading: Move unused models to storage
  • Gradient Checkpointing: Trade compute for memory
  • Mixed Precision: Use different numeric precisions
  • Memory Pooling: Reuse allocated memory

Cost Management​

Usage Monitoring​

Track resource consumption:

  • Real-time Monitoring: Live resource usage
  • Historical Analysis: Usage trends over time
  • Cost Attribution: Track costs by team/project
  • Budget Alerts: Notifications when approaching limits

Cost Optimization Strategies​

  • Spot Instances: Use discounted compute when available
  • Reserved Capacity: Pre-purchase for better rates
  • Right-Sizing: Match resources to actual needs
  • Idle Detection: Automatically shut down unused resources

High Availability​

Redundancy​

  • Multi-Zone Deployment: Spread across data centers
  • Failover Systems: Automatic failure recovery
  • Backup Resources: Standby compute capacity
  • Data Replication: Synchronized data across regions

Disaster Recovery​

  • Backup Strategies: Regular system backups
  • Recovery Procedures: Documented recovery steps
  • Testing: Regular disaster recovery drills
  • RTO/RPO: Recovery time and point objectives

Security and Compliance​

Resource Security​

  • Isolation: Secure compute environments
  • Encryption: Data encrypted at rest and in transit
  • Access Controls: Role-based resource access
  • Audit Logging: Complete resource usage logs

Compliance Features​

  • SOC 2: Security and availability controls
  • ISO 27001: Information security management
  • FedRAMP: US government cloud security
  • Industry Standards: Sector-specific compliance

Monitoring and Alerting​

Performance Metrics​

  • CPU Utilization: Processor usage percentages
  • Memory Usage: RAM and VRAM consumption
  • Network I/O: Data transfer rates
  • Disk I/O: Storage read/write operations

Dashboard Views​

  • Real-time Metrics: Live performance data
  • Historical Trends: Usage patterns over time
  • Resource Topology: Visual system architecture
  • Health Status: System component status

Integration and APIs​

Programmatic compute management is coming soon. The Mamentis API has not been published yet.

Best Practices​

Resource Planning​

  • Capacity Planning: Forecast resource needs
  • Performance Testing: Validate resource requirements
  • Gradual Scaling: Scale incrementally
  • Regular Review: Assess resource utilization

Optimization Guidelines​

  • Right-Size Resources: Match capacity to workload
  • Use Scheduling: Leverage off-peak hours
  • Monitor Continuously: Track performance metrics
  • Automate Management: Reduce manual intervention

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