Cracking the inference code: 3 proven strategies for high-performance AI

Cracking the inference code: 3 proven strategies for high-performance AI The silent killer of AI return on investment: Inefficient inference 1. Tune the engine with vLLM High-throughput serving in action 2. Optimize your models (compression and speculation) Compress without compromise Compression in action Accelerate with speculators Speculative decoding in action 3. Break the single-node barrier with distributed inference using llm-d Disaggregation: Separating prefill from decode Intelligent inference scheduling Scaling with llm-d in action Bringing it all together Learn more Red Hat AI About the authors Michael Goin Kyle Sayers Carlos Condado Megan Flynn More like this AI insights with actionable automation accelerate the journey to autonomous networks Fast and simple AI deployment on Intel Xeon with Red Hat OpenShift Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Every organization piloting generative AI (gen AI) eventually hits the "inference wall. " It’s the moment when the excitement of a working prototype meets the cold reality of production. Suddenly, that single model running on a developer’s laptop needs to serve thousands of concurrent users, maintain sub-50ms latency, and somehow not bankrupt the IT budget in cloud costs. The core challenge for enterprise AI is mainly operational: Solving the efficiency equation. It is no longer enough to just run a model, you must run it with precision performance. How do you maximize tokens per dollar? How do you make sure that a sudden spike in traffic doesn’t bring your application to a halt? In this post, we look at 3 practical strategies that help IT leaders and architects solve the inference puzzle: Optimized runtimes (vLLM) to treat your inference engine like a high-performance race car Model optimization to do more with less using compression and speculators Distributed inference (llm-d) to break the "one model, one GPU" limit and scale horizontally The math of AI inference is unforgiving. Unlike traditional microservices, LLM requests are non-uniform, stateful, and compute-intensive. A single request can occupy a GPU for seconds, not milliseconds. If your infrastructure isn't optimized, you end up over-provisioning expensive hardware just to handle peak loads, leaving GPUs idle most of the time.