Introducing Arrcus Inference Network Fabric (AINF)
Purpose-built network fabric for Distributed Inference workloads
Built for the Inference Era
INFERENCING GROWTH ENGINE FOR AI ADOPTION
Physical and Agentic AI transforming Enterprises
INFERENCING IS HIGHLY DISTRIBUTED
Latency, Power grid capacity, Data Sovereignty drive distributed infrastructure
INFERENCING IS BOTTLENECKED
Many models, many requests lead to slower inference results. Current networking solutions do not have "Policy awareness"
Intelligent “AI-policy aware” network fabric dynamically routes traffic, steers between Inference nodes, caches, datacenters
Policy Definition
Define policies based on latency targets, data sovereignty boundaries, model preference or power grid capacity
Policy Translation
AINF automatically translates policies to optimized routing paths, in real-time, to the optimal node or cache, ensuring the right inference model is delivered from the right location at the right time
Inferencing awareness and orchestration
Integrates with inferencing frameworks: vLLM, NVIDIA Dynamo, SGLang, Triton Kubernetes orchestration with prefix awareness for KV Cache optimization
Open Solution: Hardware, Load Balancers, Firewalls, CDN
Hardware agnostic solution runs on any xPU or networking hardware designed to work with Best-of-breed Load Balancers, Firewall and CDN
Results
IMPROVED THROUGHPUT
15% increase in Tokens per second (TPS)
REDUCED TIME TO FIRST TOKEN
60% reduction in TTFT
REDUCED END TO END LATENCY
40% reduction in E2EL
REDUCED COSTS
up to 30% cost reduction
