backend.ai

Lablup is now a Participating Sponsor of the Python Software Foundation (PSF).

In January 2026, the Upstage consortium that Lablup is part of successfully passed the Phase 1 evaluation for the Korean government's Sovereign AI Foundation Model project. This initiative aims to protect national AI sovereignty by having the government provide support for GPUs, data, and talent development, while the private sector actively leverages these resources to develop frontier-grade AI foundation models. We sat down with team members from Upstage and Lablup to hear the behind-the-scenes story of our Phase 1 journey.

This article covers the major changes in Backend.AI FastTrack 3 25.18.

Backend.AI 25.15 LTS is now officially available. This release brings comprehensive system-level optimization and user experience improvements, reinforcing the platform’s reliability and scalability for large-scale AI model training, deployment, and research.

We're excited to announce Backend.AI 25.6, the first Long Term Support (LTS) release of 2025. This update brings significant improvements to system monitoring, audit logging, and model service auto-scaling, making operations more convenient than ever.

Learn how KV cache offloading works in LLM serving for Agentic AI—covering architecture, data movement paths, and when offloading helps or hurts inference performance.

Lablup's research team shares what they learned building two production RAG systems over the past year: HSense, a multi-agent tariff classification system achieving 92.4% Top-1 accuracy on 10-digit HS codes, and a Backend.AI support assistant handling queries across seven documentation projects, including what didn't work and why retrieval quality matters more than model choice.

To write Storybook stories for 50+ BAI components in the Backend.AI WebUI, I started by setting up the infrastructure— i18n, theming, and branding — then upgraded to Storybook and merged two instances into one. An automation pipeline combining a 1,000-line guideline, Claude-based story generation, and GitHub Actions CI checks kept quality consistent from PR creation through deployment.