Inference Requirements

Inference differs from model training in both workflow and network behavior. Training workloads commonly generate large volumes of GPU-to-GPU traffic across backend fabrics optimized for distributed communication. Inference workloads, by contrast, are commonly dominated by request/response traffic between clients, applications, API gateways, load balancers, and inference servers.

For this reason, the frontend fabric for inference is evaluated primarily on its ability to provide predictable latency, scalable bandwidth, resilient connectivity, and operational visibility.

Table 2: Solution Requirements

Area	Inference Requirement	Frontend Fabric Impact
Latency	Inference services must respond quickly to user or application requests.	The frontend fabric must provide predictable forwarding latency and avoid unnecessary packet loss, queueing, or congestion that could increase response time.
Throughput	Inference environments must support high request concurrency and high token generation rates.	The fabric must provide scalable bandwidth between clients, load balancers, and inference servers, so traffic growth does not become a bottleneck.
Request distribution	Inference services may scale across multiple GPU servers or model-serving endpoints.	The frontend design must support connectivity to direct inference endpoints and optional load balancing services such as Envoy.
Availability	Inference services are commonly production-facing and user-facing.	The frontend design should support resilient paths, stable reachability, and operational visibility across the fabric.
Operational simplicity	Inference deployments must be easy to deploy, validate, monitor, and scale.	Intent-based automation and standardized fabric designs reduce deployment complexity and help maintain consistent operations.