3 posts tagged with "Release"

Summary​

We are excited to announce Kthena v1.0.0, a major milestone for Kubernetes-native LLM inference. This release focuses on production readiness across the serving stack: more accurate Gateway API routing, first-class role-level autoscaling for prefill/decode disaggregated workloads, safer role-level rolling updates, better router scheduling signals, session boost for multi-turn conversation workloads, richer cache-aware router observability with Prometheus metrics and example dashboards, and a more complete CLI experience.

Kthena v1.0.0 also includes an important autoscaling API consolidation. AutoscalingPolicyBinding has been removed, and target configuration now lives directly in AutoscalingPolicy through homogeneousTarget, heterogeneousTarget, and disaggregatedTarget.

Thanks to the incredible dedication and collective efforts of our contributors over the past two months, Kthena’s stability has reached new heights. We want to express our deepest gratitude to everyone who contributed to this milestone. Today, we are thrilled to announce the official release of Kthena v0.4.0—our most robust and feature-rich version yet!

Beyond rock-solid stability, Kthena v0.4.0 introduces a wave of exciting new features designed to streamline your LLM workloads and empower your AI infrastructure.

Released: 2026-01-31

Summary​

Release v0.3.0 establishes Kthena as a more robust and scalable platform for AI inference workloads. This release introduces significant enhancements in ModelServing, Router, and ModelBooster. Key highlights include seamless integration with LeaderWorkerSet, advanced network topology-aware scheduling for PD disaggregation, and a comprehensive Router Observability framework. Additionally, this version brings native ModelServing version control, support for vLLM data parallel deployment, and a complete E2E test suite for the router, ensuring high stability and reliability for production environments.