Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

The original premise of observability was to ensure system health, identify issues, and resolve those issues efficiently. As I recently outlined, the legacy approach (sometimes called Observability 1.0 now) relied heavily on metrics and tracing because logs were seen as too noisy or challenging. But, as most forward thinkers have identified now, logs are exactly the telemetry type that we need the most.

We are excited to collaborate with Dell on the white paper,Agentic RAG on Dell AI Factory with NVIDIA. The white paper is a design reference document for developers outlining strategies and solution components to implement agentic retrieval augmented generation (RAG) applications. It’s a design point for organizations across industries, specifically healthcare, for the agentic RAG framework decision-making with AI-driven data retrieval.