vLLM is a high-performance serving framework for large language models (LLMs). It optimizes token generation and resource management to deliver low-latency, scalable performance for AI-driven applications such as chatbots, virtual assistants, and recommendation systems. By efficiently managing concurrent requests and overlapping tasks, vLLM enables organizations to deploy LLMs in demanding environments with speed and efficiency.

The world of embedded systems evolves, with devices growing ever more sophisticated and software-centric. In this new landscape, with highly interconnected environments that defy traditional testing and debugging approaches, a reactive, fire-fighting mentality is no longer sufficient. Developers need a proactive strategy to gain continuous visibility into system behaviour—a strategy known as observability-driven development (ODD).

Today’s fast-paced development environments require new approaches to testing. Enter continuous performance testing. Unlike traditional performance testing, automated continuous performance testing is part of every build, monitoring the application continuously under increased load. With continuous performance tests, organizations with lots of users can expect to improve the customer experience, prevent major outages, and find and resolve issues faster.

As Gartner predicts, AI will support up to 70% of performance monitoring and troubleshooting tasks in the next few years. The Logz.io AI Agent helps teams get ahead of this curve today. Too much time spent troubleshooting? You’re not alone. Manual investigation, jumping between dashboards, and piecing together scattered data are time-consuming and frustrating.