This article details the process of building a fast vector search system for a large legal dataset (Australian High Court decisions). It covers choosing embedding providers, performance benchmarks, using USearch and Isaacus embeddings, and the importance of API terms of service. It focuses on achieving speed and scalability while maintaining reasonable accuracy.
An effort to create a fully functional Kubernetes cluster with 1 million active nodes. The article details the challenges and solutions for scaling Kubernetes to this size, covering networking, state management (etcd), and the scheduler.
vLLM Production Stack provides a reference implementation on how to build an inference stack on top of vLLM, allowing for scalable, monitored, and performant LLM deployments using Kubernetes and Helm.
This article dives into designing a scalable distributed job scheduling service that can handle millions of tasks. It covers system components, API design, scaling strategies, handling failures, and addressing single points of failure.
High-performance deployment of the vLLM serving engine, optimized for serving large language models at scale.
