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Real-Time Edge Intelligence Fabric
Low-Latency AI Networks at the Edge
Edge AI requires specialized network architecture that minimizes latency and maximizes reliability. Hedgehog Edge Intelligence Fabric provides the networking foundation for real-time decision making directly at the point of data collection.
Network advantages for edge AI workloads:
- Ultra-low latency connectivity eliminates the need for cloud round-trips
- Resilient local clusters ensure continuous operation even during connectivity interruptions
- Support for commodity hardware provides cost-effective infrastructure without vendor lock-in
- Zero-touch lifecycle management (ZTLM) automates installation, configuration, and ongoing operations
Perfect for networking smart camera arrays in manufacturing environments, 5G-connected drone fleets requiring real-time coordination, mobile command centers and other edge solutions where real-time intelligence needs requires consistent local networking performance.
Retrieval-Augmented Generation (RAG) Fabric
Balanced Networks for Hybrid Workloads
RAG architectures demand consistent performance across complex network paths. Hedgehog RAG Fabric delivers a balanced Clos network topology specifically tuned for the unique traffic patterns between databases and language model servers.
Network advantages for RAG workloads:
- Optimized traffic distribution for high-volume database queries
- Balanced east-west bandwidth ensures consistent cross-component communication
- Simplified network expansion as vector database requirements grow
- Low-latency routing across the network fabric
Perfect for network infrastructure supporting enterprise search applications, customer support systems with complex query patterns, or code assistance platforms requiring rapid data retrieval and processing.
AI Fine-Tuning Fabric
Networks That Handle Gradient Bursts
Fine-tuning creates unpredictable network traffic patterns. Hedgehog Fine-Tuning Fabric automatically configures lossless QoS policies via EVPN, ensuring critical traffic flows remain unaffected by microburst congestion.
Network advantages for fine-tuning workloads:
- Prioritized gradient synchronization prevents packet loss during traffic spikes
- Faster training convergence through optimized network parameters
- Automated QoS configuration with pre-tuned policies for AI workloads
- Rapid network provisioning through streamlined API calls
Perfect for research labs adapting foundation models to specialized domains, financial institutions running nightly domain-specific training, or healthcare organizations fine-tuning diagnostic models where consistent network performance directly impacts model convergence time and quality.
AI Training Fabric
Purpose-Built Networking for AI Model Training
High-performance AI training demands specialized network architecture. Hedgehog Training Fabric delivers a dual-path network design that intelligently manages compute and storage traffic—all through our intuitive cloud-native API.
Network advantages for training workloads:
- Optimized training throughput with specialized fabric configuration for low-latency network communication
- Segregated traffic paths prevent storage operations from impacting training traffic
- Flexible network scaling with support for multiple topologies as your cluster grows
- Zero-touch BGP-EVPN configuration with automated installation and provisioning
From pharmaceutical QA labs running distributed training to AI research centers requiring multi-rack infrastructure, Hedgehog Training Fabric ensures your network architecture never becomes the bottleneck in your AI pipeline.
High-Performance Data Center Fabric
Enterprise-Grade Networking Made Simple
Modern data centers run diverse workloads with varying network requirements. Hedgehog Data Center Fabric delivers enterprise-grade traffic isolation and consistent performance through a single, intuitive cloud-native API.
Network advantages for mixed workloads:
- Automated tenant isolation with VPC-based network segmentation
- Performance-based traffic management ensures predictable application response
- Simplified capacity expansion with spine-leaf architecture
- Advanced traffic classification for diverse application requirements
Perfect for connecting infrastructure in multi-tenant environments, enterprise data centers with varied application demands, or computing clusters requiring precise resource allocation and isolation.
