Inference: AI's Inflection Point

As AI inference becomes the dominant driver of AI data center demand, organizations face growing pressure to optimize performance, scalability, and operational cost across the inference lifecycle. While AI model training represents a one‑time investment, AI inference introduces recurring operational expenditure, making validation and optimization critical as large language models (LLMs) are deployed across a wide range of workflows.

Keysight AI Inference Builder (KAI‑IB) enables organizations to validate and optimize AI inference infrastructures using realistic workload emulation and analytics. AI inference traffic traverses a complex infrastructure chain that includes front‑end network solutions, AI security and guardrail controls, SmartNICs and DPUs, and AI inference hardware and software stacks. Within the inference pipeline, stages such as tokenization, prefill, decode, and detokenization place different demands on GPU compute, memory bandwidth, and memory capacity. Under high concurrency, long‑context sessions, or multi‑turn conversations, these factors can lead to latency increases, reduced throughput, dropped requests, or underutilized GPU resources.

Keysight AI Inference Builder generates high‑fidelity LLM inference traffic that mirrors real user behavior, scales to thousands of users or prompts per second, and correlates inference‑native metrics with system‑level GPU telemetry. This enables teams to identify bottlenecks, evaluate scalability limits, and assess performance, cost‑per‑token, and user experience under production‑like conditions.

NVIDIA DSX AIR provides the AI factory simulation environment in which AI infrastructure can be designed, modeled, and validated before physical deployment. DSX AIR allows routing and switching fabrics, SmartNIC and DPU offloads, compute resources, memory systems, storage platforms, and security frameworks to be evaluated together as a cohesive architecture.

Together, Keysight AI Inference Builder and NVIDIA DSX AIR enable organizations to simulate, validate, and optimize AI inference environments from early design through production deployment, reducing risk while accelerating the path to scalable and efficient AI infrastructure.