Akamas

Same infrastructure. 53% more AI throughput. Our study shows what optimal inference configuration actually unlocks at the GPU layer.

40-60% of K8s spend is typically recoverable. See how much of yours is - in 90 seconds.

Not what it bills. What it wastes. Calculate your annual overspend estimate, benchmarked against environments of your size.

Most K8s environments have 8–15 workloads at risk from misconfigured resources. Get your estimate in 90 seconds.

Free webinar on AI inference & GPU optimization on K8s - next week, June 3rd, 3PM CEST - 9AM EST! We're going to talk about how to make the most out of your existing GPUs and serve more users without spending more $$$. At the same time, optimizing inference performance is key to drive adoption of AI use cases - a slow or unreliable service means customer churn. Last but not least, accuracy: If your model (or agent) quality is not good, the overall value of your AI solution will be impacted. Optimizing AI workloads is not a walk in the park: it's a whole new performance engineering game in terms of complexity and skills. But if done right, the impact is huge. I'm super glad to discuss those topics with Daniele Zonca and Roland Huß, Distinguished Engineers from the Red Hat Openshift AI team. Daniele and Roland have a front seat in the development of llm-d, the new AI distributed serving stack for K8s, and they even wrote a book on these topics. Register here: https://lnkd.in/dMifd2yd #GenAI #K8s #GPU #PerformanceEngineering

📅 Save the date: June 3, 15:00 CEST Red Hat and Akamas are teaming up for a 1-hour webinar on running GenAI workloads efficiently on #Kubernetes. We won't pitch products. We'll walk through the real optimization challenges: GPU infrastructure right-sizing, cost savings in inference serving, agent tracing, and the cost-latency-accuracy tradeoffs that make GenAI optimization unique. Save your seat 👇