Get Ready for the Inference Era: Introducing Keysight AI Inference Builder
The first leg of the AI race has been run — and it was an all-out sprint to train the biggest, fastest, most complex models possible. Now, we’re entering the second leg, and operators have shifted focus. This part of the race is all about inference.
In layman’s terms, inference is how AI models respond to user prompts and queries. But, to a more experienced eye, the truth is somewhat more substantial: inference is where AI becomes a service your users depend on, and where infrastructure complexity becomes visible.
The same model that looks stable in a lab might struggle in production because production behavior is just different. Prompt diversity, concurrency, and token streaming are all variable, and retrieval and guardrails are in the path — causing models to miss latency targets with volatile throughput and high costs per response.
The problem stems from testing. Inference is an inline pipeline across compute, memory, storage, networking, security, and orchestration — and the weakest link determines performance, stability, and economics. There’s so much variability and volatility within those workflows that it’s hard to isolate bottlenecks before they reveal themselves in production. Because of this, most teams resort to using generic load tools or isolated GPU benchmarks. However, these approaches aren’t capable of reproducing the nuances of inference-native behavior — such as prefill and decode dynamics, token bursts, multi-turn interactions, and adversarial prompt patterns. Bottlenecks persist because, until now, the industry hasn’t had a simple solution to find and remediate them.
That all changes today.
Meet KAI Inference Builder
Real-World Prompt Behavior
Prompt behavior isn’t generic; it’s a chameleon that shape-shifts to match each model. A retail banking request does not look like an academic query. A legal workflow carries different structure, length, and sensitivity. KAI Inference Builder starts here, with real-world prompt behavior profiles matching models in law firms, finance, academia, and beyond. Keysight research teams build these profiles, so your team doesn’t need to assemble prompt taxonomies and distributions from scratch. You open a profile, review its sub profiles, then tune the mix to match your deployment.
Fine-Tune Deployments by “Talking” to Inference Stacks
KAI Inference Builder helps you have a real conversation with your inference stack. By sending realistic prompts and measuring how the system responds across compute, memory, networking, and traffic behavior, it reveals what the stack needs to perform at its best. You can see where latency builds, where resources are under pressure, and where tuning can improve throughput or efficiency. Instead of guessing, you get clear, actionable feedback from the stack itself — making it easier to fine-tune deployments, validate changes, and move forward with greater confidence.
Shift to Technology Benchmarking for Focus
Sometimes you need to isolate what part of the stack is limiting throughput. That’s why KAI Inference Builder includes inference stack benchmarking so you can target specific components and subsystems. You can validate a GPU cluster, KV cache behavior, memory and storage pressure, inference pipelines, or networking to and between GPUs. In this mode, Keysight emulates the client-side traffic so you can analyze how workloads shape demand, fine-tune specific parts of the stack, and compare results across changes.
Use End-to-End Validation When You Need to Evaluate the Full Inference Pipeline
Many inference failures show up only when client and server dynamics run together. So we built KAI Inference Builder to support end-to-end validation, where the tool emulates both prompt and response dynamics at scale. That includes scenarios where you need sender and receiver coverage, plus the surrounding behaviors teams often skip during lab testing. The result is validation that maps to the unique realities of real-world inference deployments, not generalizations drawn from a simplified, one-size-fits-all test harness.
Why This Matters Now
Inference performance is no longer defined by peak GPU throughput; it’s defined by balance and predictability under real workloads. KAI Inference Builder helps you reproduce the behaviors your users create, measure the experience they receive, and correlate those outcomes with the signals that explain why. That is how teams reduce surprises, accelerate deployment decisions, and keep inference services stable as demand grows.