<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Kukie Serving on History</title><link>https://kukie.tech/series/kukie-serving/</link><description>Recent content in Kukie Serving on History</description><generator>Hugo</generator><language>ko-kr</language><lastBuildDate>Tue, 03 Nov 2026 10:00:00 +0900</lastBuildDate><atom:link href="https://kukie.tech/series/kukie-serving/index.xml" rel="self" type="application/rss+xml"/><item><title>Kukie Serving.03 ServedModel Operator</title><link>https://kukie.tech/posts/kukie-serving-03/</link><pubDate>Tue, 03 Nov 2026 10:00:00 +0900</pubDate><guid>https://kukie.tech/posts/kukie-serving-03/</guid><description>Building kukie-oven, a Go operator that turns one ServedModel into a PVC, a download Job and a vLLM Deployment — the reconcile order, two server-side-apply field managers, telling someone else&amp;rsquo;s object from a leftover, Helm and GHCR packaging, an EBS CSI role limited by a tag boundary, and the first real run on an L40S: Ready in 14 minutes 11 seconds with no manual step.</description></item><item><title>Kukie Serving.02 GPU Add-on Design</title><link>https://kukie.tech/posts/kukie-serving-02/</link><pubDate>Tue, 03 Nov 2026 09:00:00 +0900</pubDate><guid>https://kukie.tech/posts/kukie-serving-02/</guid><description>Why an external LLM API plan became a Helm add-on for users&amp;rsquo; GPU clusters — quality and cost numbers behind the pivot, three layers split by required permissions, a chart with per-layer switches and a preflight job, the measurement-to-429 loop, an honest comparison with KubeAI and LiteLLM, and what is built versus still planned.</description></item><item><title>Kukie Serving.01 Baseline on One L40S</title><link>https://kukie.tech/posts/kukie-serving-01/</link><pubDate>Mon, 26 Oct 2026 02:00:00 +0900</pubDate><guid>https://kukie.tech/posts/kukie-serving-01/</guid><description>Measuring how many concurrent requests one NVIDIA L40S serves within a latency SLA before building a GPU cluster — the SLA draft as TTFT and inter-token latency, a single g6e.xlarge with two stop guards, a measurement script split from the server so it can run as a Kubernetes Job, results for four open models, why MoE models outrun a dense 27B on memory bandwidth, KV cache FP8 for 20% more headroom, and two review fixes.</description></item></channel></rss>