Swarm: Decentralized Cloud for AI
  • Introduction
    • The Problem
    • How Swarm works
    • Built for AGI
  • Market Opportunity
  • Key Benefits
  • Competitive Landscape
  • Primary Market Segments
  • Value Proposition
  • Core Technologies
  • System Architecture
    • System Layers
    • Core Components
    • Resource Types
    • Node Specifications
    • Ray Framework Integration
    • Kubernetes Integration
  • AI Services
  • High Availability Design
    • Redundancy Architecture
    • Failover Mechanisms
    • Resource Optimization
    • Performance Metric
  • Privacy and Security
    • Defense in Depth Strategy
    • Security Layer Components
    • Confidential Computing: Secure Enclave Architecture
    • Secure Enclave Architecture
    • Data Protection State
    • Mesh VPN Architecture: Network Security
    • Network Security Feature
    • Data Privacy Framework
    • Privacy Control
  • Compliance Framework: Standards Support
    • Compliance Features
  • Security Monitoring
    • Response Procedures
  • Disaster Recovery
    • Recovery Metrics
  • AI Infrastructure
    • Platform Components
    • Distributed Training Architecture
    • Hardware Configurations
    • Inference Architecture
    • Inference Workflow
    • Serving Capabilities
    • Fine-tuning Platform
    • Fine-tuning Features
    • AI Development Tools
    • AI Development Features
    • Performance Optimization
    • Performance Metrics
    • Integration Architecture
    • Integration Methods
  • Development Platform
    • Platform Architecture
    • Development Components
    • Development Environment
    • Environment Features
    • SDK and API Integration
    • Integration Methods
    • Resource Management
    • Management Features
    • Tool Suite: Development Tools
    • Tool Features
    • Monitoring and Analytics
    • Analytics Features
    • Pipeline Architecture
    • Pipeline Features
  • Node Operations
    • Provider Types
    • Provider Requirements
    • Node Setup Process
    • Setup Requirements
    • Resource Allocation
    • Management Features
    • Performance Optimization
    • Performance Metrics
    • Comprehensive Security Implementation
    • Security Features
    • Maintenance Operations
    • Maintenance Schedule
    • Provider Economics
    • Economic Metrics
  • Network Protocol
    • Protocol Layers
    • Protocol Components
    • Ray Framework Integration
    • Ray Features
    • Mesh VPN Network
    • Mesh Features
    • Service Discovery
    • Discovery Features
    • Data Transport
    • Transport Features
    • Protocol Security
    • Security Features
    • Performance Optimization
    • Performance Metrics
  • Technical Specifications
    • Node Requirements
    • Hardware Specifications
    • Network Requirements
    • Network Specifications
    • Key Metrics for Evaluating AI Infrastructure
    • Metrics and Service Level Agreements (SLAs)
    • Security Standards
    • Security Requirements
    • Scalability Specifications
    • System Growth and Capacity
    • Compatibility Integration
    • Compatibility Matrix: Supported Software and Integration Details
    • Resource Management Framework
    • Resource Allocation Framework
  • Future Developments
    • Development Priorities: Goals and Impact
    • Roadmap for Platform Enhancements
    • Research Areas for Future Development
    • Strategic Objectives and Collaboration
    • Infrastructure Evolution Roadmap
    • Roadmap for Advancing Core Components
    • Market Expansion Framework
    • Expansion Targets: Strategic Growth Objectives
    • Integration Architecture: Technology Integration Framework
    • Integration Roadmap: Phased Approach to Technology Integration
  • Reward System Architecture: Network Incentives and Rewards
    • Reward Framework
    • Reward Distribution Matrix: Metrics and Weighting for Equitable Rewards
    • Hardware Provider Incentives: Performance-Based Rewards Framework
    • Dynamic Reward Scaling: Adaptive Incentive Framework
    • Resource Valuation Factors: Dynamic Adjustment Model
    • Network Growth Incentives: Expansion Rewards Framework
    • Long-term Incentive Structure: Rewarding Sustained Contributions
    • Performance Requirements: Metrics and Impact on Rewards
    • Sustainability Mechanisms: Ensuring Economic Balance
    • Long-term Viability Factors: Ensuring a Scalable and Sustainable Ecosystem
    • Innovation Incentives: Driving Technological Advancement and Network Growth
  • Network Security and Staking
    • Staking Architecture
    • Stake Requirements: Ensuring Commitment and Security
    • Security Framework: Network Protection Mechanisms
    • Security Components: Key Functions and Implementation
    • Monitoring Architecture: Real-Time Performance and Security Oversight
    • Monitoring Metrics: Key Service Indicators for Swarm
    • Risk Framework: Comprehensive Risk Management for Swarm
    • Risk Mitigation Strategies: Proactive and Responsive Measures
    • Slashing Conditions: Penalty Framework for Ensuring Accountability
    • Slashing Matrix: Violation Impact and Recovery Path
    • Network Protection: Comprehensive Security Architecture
    • Security Features: Robust Mechanisms for Network Integrity
    • Recovery Framework: Ensuring Resilience and Service Continuity
    • Recovery Process: Staged Actions for Incident Management
    • Security Governance: Integrated Oversight Framework
    • Control Framework: A Comprehensive Approach to Network Governance and Security
  • FAQ
    • How Swarm Parallelizes and Connects All GPUs
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  1. Technical Specifications

Resource Allocation Framework

Resource Limits: Comprehensive Resource Allocation Framework

Swarm’s Resource Limits establish boundaries for resource allocation at the user, node, and cluster levels. These limits ensure fair usage, optimized performance, and scalability across its decentralized infrastructure, catering to diverse workloads while supporting future growth.

Resource

Per User

Per Node

Per Cluster

GPUs

16

32

1,000

vCPUs

64

128

10,000

Memory

512GB

1TB

100TB

Storage

10TB

100TB

10PB

Descriptions

  1. GPUs:

    • Per User: Up to 16 GPUs allocated for individual workloads or experiments.

    • Per Node: Nodes can support up to 32 GPUs, ideal for large-scale training tasks.

    • Per Cluster: Clusters can aggregate up to 1,000 GPUs for distributed deep learning and compute-intensive applications.

  2. vCPUs:

    • Per User: Allows up to 64 virtual CPUs for lightweight or multi-threaded tasks.

    • Per Node: Supports up to 128 vCPUs, providing sufficient capacity for diverse workloads.

    • Per Cluster: Scales to 10,000 vCPUs, enabling large-scale distributed computing.

  3. Memory:

    • Per User: Up to 512GB for memory-intensive applications like large model training or data analytics.

    • Per Node: Provides 1TB for high-performance nodes catering to specialized tasks.

    • Per Cluster: Aggregates 100TB, supporting massive datasets and complex simulations.

  4. Storage:

    • Per User: Allocates up to 10TB for datasets, model checkpoints, and logs.

    • Per Node: Scales up to 100TB for storage-heavy nodes handling extensive data.

    • Per Cluster: Enables 10PB for distributed data storage across large-scale deployments.

Key Features

  • Scalability: Limits support incremental growth from single-user tasks to cluster-wide workloads.

  • Flexibility: Supports a variety of use cases, from small-scale experiments to enterprise-level operations.

  • Fair Usage: Ensures equitable distribution of resources across users and nodes.

  • High Capacity: Provides ample headroom for large-scale AI workloads and future expansion.

Benefits

  • Efficiency: Optimized resource allocation prevents over-provisioning and underutilization.

  • Reliability: Consistent resource limits ensure predictable performance across deployments.

  • Adaptability: Accommodates diverse user needs while supporting scalability for future growth.

  • Robustness: Sufficient capacity for intensive tasks ensures operational reliability even under high demand.

These Resource Limits form a foundational framework for building, operating, and scaling the Swarm platform, ensuring security, consistency, and flexibility for evolving AI workloads.

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Last updated 4 months ago