Scaling Kubernetes to 2,500 nodes
Read the original on OpenAI Blog →The Flow has not summarised this story yet — read it at OpenAI Blog.
The Flow has not summarised this story yet — read it at OpenAI Blog.
We’ve scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as Scaling Laws for Neural Language Models.
arXiv:2607. 25995v1 Announce Type: cross Abstract: Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads.
An inside look at how OpenAI scaled PostgreSQL to millions of queries per second using replicas, caching, rate limiting, and workload isolation.
arXiv:2607. 21623v1 Announce Type: new Abstract: We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal prediction with finite-sample-corrected Adaptive Prediction Sets, calibration assessment, drift detection via RFF-approximated Maximum Mean Discrepancy, fairness monitoring with bootstrap confidence intervals, a DAG-based pipeline orchestrator, and a result storage API.
This is the opening piece of a four-part deep dive series, on building a high-frequency streaming pipeline against a live public API. The data source is openSenseMap, a citizen-science IoT network used for climate research, mostly in Germany.
arXiv:2606. 03323v2 Announce Type: replace-cross Abstract: The rise of LLM-as-a-Service and other confidential cloud workloads demands cryptographic proof that user data is processed in a trusted, untampered environment.