Inference Network Fabric
The Intelligence Layer for AI Inference
AINF dynamically routes AI inference traffic across distributed infrastructure — optimizing for latency, cost, sovereignty, and power. Purpose-built for the inference era.
AI Inference Catalyst
Physical and Agentic AI are transforming enterprises — driving explosive demand for distributed, always-on inference infrastructure.
Inference Is Decentralized
Latency requirements, power grid constraints, and data sovereignty rules are forcing inference to run across multiple sites and clouds.
Inference Is Bottlenecked
Many models, many requests — but current networks have no “policy awareness.” Every request is treated the same, regardless of latency, cost, or compliance needs.
Intelligent AI-Aware Fabric
Just as CDNs transformed how content is delivered, Inference Delivery Networks (IDNs) are redefining how AI workloads are routed globally. AINF is the software fabric that powers them.
Policy Definition
Define routing policies based on latency targets, data sovereignty boundaries, model availability, and power grid capacity — without writing a line of network code.
Policy Translation
AINF automatically translates business policies into optimized real-time routing paths — directing every request to the right model, node, or cache at the right time.
Inferencing Awareness and Orchestration
Native integrations with vLLM, NVIDIA Dynamo, SGLang, and Triton. Kubernetes-aware orchestration with prefix-based KV Cache optimization built in.
Open Solution: Hardware, Load Balancers, Firewalls, CDN
Hardware-agnostic. Runs on any XPU or networking hardware. Works with best-of-breed load balancers, firewalls, and CDN providers — no lock-in.
Measurable Results
AINF delivers real-world performance improvements validated in production environments.
More Tokens Per Second
Capacity-aware routing improves throughput across heterogeneous GPU fleets. Source: Anyscale Ray Serve
Reduction in Time to First Token
Intelligent routing minimizes queue time and model cold-start latency. Source: Red Hat vLLM Semantic Routing
Reduction in End-to-End Latency
Policy-driven path selection cuts round-trip time across distributed sites.
Lower Cost Per Inference
Smarter routing reduces wasted compute and token retrieval overhead. Source: AWS Machine Learning Blog
Learn More About AINF
PRESS RELEASE
Arrcus announces AINF integration with NVIDIA Dynamo, BlueField DPUs and Spectrum