LLMKube – A Kubernetes operator for local LLMs across Nvidia and Mac fleets
LLMKube is a Kubernetes operator designed for deploying local large language models (LLMs) on Nvidia and Apple Silicon hardware. The latest version, 0.7.9, introduces a new mlx-server runtime and improved scaling support. This platform aims to simplify the deployment and management of LLMs, making it easier for teams to prototype and scale their applications.
- ▪LLMKube allows users to run production LLMs on their own hardware.
- ▪Version 0.7.9 adds mlx-server as a runtime option and kubectl scale support.
- ▪The platform supports various runtimes including vLLM, TGI, and llama.cpp.
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| Original publisher | LLMKube |
| Canonical URL | https://llmkube.com/ |
| Publication time | Sat, 23 May 2026 13:17:29 +0000 |
| Retrieval time | 2026-05-23T13:37:26.771Z |
| Last seen | 2026-05-23T13:37:26.771Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 2-1Q1MIuD984 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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v0.7.9 Open Source · Kubernetes Native · NVIDIA + Apple Silicon · vLLM + llama.cpp + mlx-server Run production LLMs on your own hardware A Kubernetes operator for self-hosted LLM inference. vLLM, llama.cpp, TGI, NVIDIA, Apple Silicon. Recently a local model on two $400 GPUs wrote its own next feature, merged as PR #283. Get Started View on GitHub Star Join the Discord • What's new in 0.7.9: a new mlx-server runtime for Apple Silicon, plus kubectl scale support → See it in action Deploy LLMs with any runtime in seconds using the llmkube CLI terminal $ llmkube deploy llama-3.1-8b --gpu --runtime vllm 🚀 Deploying LLM inference service ═══════════════════════════════════════════════ Name: llama-3.1-8b Runtime: vllm Accelerator: cuda GPU: 2 x nvidia 📦 Creating Model 'llama-3.1-8b'...
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Excerpt limited to ~120 words for fair-use compliance. The full article is at LLMKube.
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