Network & Infrastructure
Our robust network design and infrastructure implementation ensures high-performance, reliable, and secure connectivity for AI hosting environments.
Back to PlaybookNetwork & Infrastructure Overview
The ComputeComplete Network & Infrastructure framework provides a comprehensive approach to designing, implementing, and managing high-performance network infrastructure specifically optimized for AI workloads. Our methodology ensures that converted mining facilities meet or exceed the connectivity, reliability, and security requirements of enterprise AI customers.
Network Design Principles
- High Performance: Network infrastructure designed to deliver maximum throughput and minimal latency for demanding AI workloads.
- Redundancy: Multiple paths and components throughout the network to eliminate single points of failure and ensure continuous operation.
- Scalability: Modular design that allows for seamless expansion as capacity requirements grow without disrupting existing operations.
- Security: Comprehensive security controls integrated at all layers of the network to protect against unauthorized access and attacks.
- Manageability: Centralized monitoring and management systems that provide visibility and control across the entire network infrastructure.
Core Infrastructure Components
Dual-Fiber Entry
Physically diverse fiber entry points to ensure continuous connectivity even in the event of a fiber cut or carrier outage.
Key Features:
- Geographically diverse entry points
- Separate conduit paths to property line
- Redundant carrier handoff rooms
- Automated path failover
- 24/7 monitoring and alerting
Redundant Meet-Me Rooms
Separate physical spaces for carrier equipment and cross-connects to ensure isolation and redundancy.
Key Features:
- Physically separated rooms with independent systems
- Redundant power and cooling
- Diverse cable pathways to data halls
- Carrier-neutral connectivity options
- Comprehensive fire suppression systems
Secure VLAN Segmentation
Logical network segmentation to isolate customer environments and control traffic flow between network segments.
Key Features:
- IEEE 802.1Q VLAN implementation
- Private VLANs for customer isolation
- Micro-segmentation capabilities
- Traffic filtering between segments
- Automated VLAN provisioning
VPN Remote Management
Secure remote access infrastructure for management and monitoring of network and systems.
Key Features:
- Site-to-site and client VPN options
- Multi-factor authentication
- Encrypted tunnels with perfect forward secrecy
- Granular access controls
- Comprehensive connection logging
Network Architecture Diagram
Network Architecture Diagram Would Be Displayed Here
Key Components:
- Dual Fiber Entry Points: Physically diverse paths for carrier connectivity
- Redundant Meet-Me Rooms: Separate physical spaces for carrier equipment
- Core Network Fabric: High-performance, non-blocking network core
- Distribution Layer: Redundant connections to each rack row
- Access Layer: High-density ToR switches with redundant uplinks
- Security Perimeter: Next-generation firewalls with advanced threat protection
Implementation Methodology
Our network infrastructure implementation follows a structured methodology to ensure comprehensive coverage and optimal performance:
Network Assessment
Comprehensive evaluation of existing network infrastructure, identification of reusable components, and gap analysis against AI hosting requirements.
Architecture Design
Development of a tailored network architecture blueprint that addresses identified gaps and aligns with performance, reliability, and security requirements.
Carrier Selection
Evaluation and selection of telecommunications carriers that best meet the bandwidth, latency, and reliability requirements of AI workloads.
Equipment Selection
Selection of network equipment (switches, routers, firewalls) that provides the necessary performance, features, and scalability.
Implementation Planning
Development of detailed implementation plans, including timelines, resource requirements, and risk mitigation strategies.
Phased Deployment
Structured implementation of network infrastructure in prioritized phases to minimize operational disruption.
Testing & Validation
Comprehensive testing of all network components and end-to-end performance validation to ensure requirements are met.
Documentation & Training
Development of detailed network documentation and comprehensive training for operations staff.
Case Study: High-Density AI Network
A former cryptocurrency mining facility in Washington state required a network infrastructure upgrade to support high-density AI compute clusters with demanding bandwidth and latency requirements.
Our team implemented a comprehensive network solution that included:
- 400G backbone network with non-blocking architecture for maximum throughput
- Direct connectivity to three Tier 1 carriers with automated failover
- RDMA over Converged Ethernet (RoCE) implementation for ultra-low latency
- Advanced QoS implementation to prioritize AI workload traffic
Result: The facility achieved consistent sub-5ms latency and sustained 100Gbps+ throughput for AI model training workloads, enabling the client to secure contracts with premium AI research organizations.
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