Arrcus Inference Network Fabric
AI-Policy-Aware Networking for Distributed Inference
Deliver real-time and agentic AI experiences with a purpose-built inference network fabric that dynamically steers AI traffic across inference nodes, caches, and datacenters—optimizing
Built for the Inference Era
Inference is now the fastest-growing segment of AI infrastructure—and the network has become the bottleneck. AI inference workloads are increasingly distributed across edge locations, regional datacenters, and centralized AI hubs, each with different latency, power, cost, and data sovereignty constraints. Arrcus Inference Network Fabric (AINF) is a software-defined, AI-policy-aware fabric designed to intelligently route inference traffic so the right model is delivered from the right location at the right time.
The Challenge
Modern inference environments face growing complexity:
- Power-driven decentralization across regions and edge sites
- Latency-sensitive and agentic AI workloads
- Uneven site capabilities and distributed model placement
- Inefficient utilization of inference infrastructure
- Data sovereignty and regulatory requirements that don’t scale
Traditional hardware-defined networks lack the intelligence and flexibility required to meet these demands.
The AINF Advantage
AINF introduces an AI-aware routing fabric that understands inference intent, application service-level objectives, and infrastructure constraints in real time.
Operators define policies—such as latency targets, power limits, data residency boundaries, or model preferences—and AINF continuously evaluates network conditions, site load, and resource availability to dynamically steer inference traffic to the optimal node or cache. This results in:
- Faster time to first token
- Higher inference throughput
- Lower end-to-end latency
- Reduced cost per inference
How it Works
At its core, AINF introduces a policy abstraction layer that translates inference application intent into real-time network decisions—without exposing operators to infrastructure complexity. AINF evaluates:
Inference request semantics and SLOs
Model availability and placement
Network latency and congestion
Site load and power capacity
Data sovereignty and compliance boundaries
Based on these inputs, inference traffic is dynamically routed to the optimal location to meet performance, cost, and regulatory requirements.
Key Capabilities
AI-Aware Traffic Steering
Extracts inference semantics and service-level objectives directly from requests to make intelligent routing decisions.
KV Cache & Prefix Awareness
Optimizes KV cache utilization to reduce token retrieval time and improve throughput for large-scale inference workloads.
Policy-Driven Model Selection
Routes inference traffic based on latency, cost, power availability, model preference, and sovereignty constraints.
Distributed, Multi-Site Scale
Designed for inference across edge, regional, and centralized datacenters.
Open & Disaggregated Architecture
Runs on best-of-breed xPUs and network silicon across hardware vendors—without lock-in.
Integrated with Modern Inference Frameworks
AINF integrates with leading inference frameworks such as vLLM, SGLang, and NVIDIA Triton, enabling tight coupling between model orchestration and intelligent network steering.
This ensures optimal model selection and consistent performance across distributed inference clusters.
Built on Arrcus AI Networking Leadership
AINF builds on Arrcus’ proven leadership in AI and datacenter networking. The Arrcus ACE-AI platform already delivers a unified fabric for distributed AI across datacenter, edge, and hybrid cloud environments.
AINF extends this foundation with inference-specific intelligence—while maintaining Arrcus’ commitment to open, software-defined networking.
Partner-Ready by Design
AINF is designed to integrate with partner ecosystems, allowing operators to incorporate:
- Load balancers
- Firewalls
- Power management systems
This enables secure, optimized inference delivery and AI-aware content distribution across distributed environments.
Why Arrcus Inference Network Fabric
Inference performance is no longer limited by compute alone—it’s constrained by where models run, how traffic is routed, and which policies are enforced. AINF turns the network into an active participant in AI inference.
Optimal Inference Steering
AI-policy-aware traffic steering across distributed inference environments
Accelerated Token Delivery
Lower latency and faster time-to-first-token for real-time and agentic AI
Hybrid Infrastructure Synergy
Improved infrastructure utilization across edge and datacenter sites
Energy-Aware Inference
Built-in support for data sovereignty and power constraints
Defined Network Freedom
Open, software-defined networking with no vendor lock-in
Power the next generation of AI inference with intelligent, policy-driven networking
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