--- title: "LLMKube vs ggrun" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/defilantech-llmkube-vs-raketenkater-ggrun" tools: ["defilantech-llmkube", "raketenkater-ggrun"] --- # LLMKube vs ggrun *GraphCanon updated Sep 20, 2026* ## Verdict Pick LLMKube if lLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim; pick ggrun if ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. [LLMKube](https://llmkube.com) reports 210 GitHub stars, 33 forks, and 98 open issues, last pushed Sep 17, 2026. [ggrun](https://github.com/raketenkater/ggrun) has 275 stars, 18 forks, and 4 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [LLMKube's repository](https://github.com/defilantech/LLMKube) and [ggrun's repository](https://github.com/raketenkater/ggrun). | | [LLMKube](/tools/defilantech-llmkube.md) | [ggrun](/tools/raketenkater-ggrun.md) | | --- | --- | --- | | Tagline | Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets | Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server | | Stars | 210 | 275 | | Forks | 33 | 18 | | Open issues | 98 | 4 | | Language | Go | Go | | Adopt for | LLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim | ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMKube](/tools/defilantech-llmkube.md) | [ggrun](/tools/raketenkater-ggrun.md) | | --- | --- | --- | | Open issues (now) | 98 | 4 | | Stars delta | +27 (30d) | +11 (30d) | | Open issues delta | +21 (30d) | +3 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/defilantech-llmkube/trust.md) | [trust report](/tools/raketenkater-ggrun/trust.md) | ## Decision facts: LLMKube - **Pricing:** freemium - Free and open-source under the Apache-2.0 license. - **Requirements:** Requires a Kubernetes cluster for installation and operation.; Supports deployment on a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. - **Adopt for:** LLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim - **License detail:** Apache-2.0 ## Decision facts: ggrun - **Pricing:** freemium - Free to use under MIT license; no direct costs involved in usage. - **Adopt for:** ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. - **License detail:** MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved. ## Choose when ### Choose LLMKube if… - License: LLMKube is Apache-2.0, ggrun is MIT. - Pricing: Free and open-source under the Apache-2.0 license.. - Requirements: Requires a Kubernetes cluster for installation and operation.; Supports deployment on a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.. - Tags unique to LLMKube: ai, apple-silicon, autoscaling, edge-computing. - LLMKube ships Docker support for self-hosted deployment. - When you need to deploy and manage self-hosted large language model inference services across a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. ### Choose ggrun if… - License: ggrun is MIT, LLMKube is Apache-2.0. - Pricing: Free to use under MIT license; no direct costs involved in usage.. - Tags unique to ggrun: cuda, golang, inference-server, llama-cpp. - When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs ## When NOT to use LLMKube - If your infrastructure does not include a Kubernetes cluster, as LLMKube is designed to operate within a Kubernetes environment. - If you are looking for a solution that does not support a heterogeneous mix of GPU architectures, LLMKube is specifically designed to work with NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. - If you do not require or prefer not to use the specific runtimes supported by LLMKube, such as llama.cpp, vLLM, TGI, and mlx-server. ## When NOT to use ggrun - For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity - When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun ## Common questions ### What is the difference between LLMKube and ggrun? LLMKube: Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets. ggrun: Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMKube over ggrun? Choose LLMKube over ggrun when License: LLMKube is Apache-2.0, ggrun is MIT; Pricing: Free and open-source under the Apache-2.0 license.; Requirements: Requires a Kubernetes cluster for installation and operation.; Supports deployment on a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.; Tags unique to LLMKube: ai, apple-silicon, autoscaling, edge-computing; LLMKube ships Docker support for self-hosted deployment; When you need to deploy and manage self-hosted large language model inference services across a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. ### When should I choose ggrun over LLMKube? Choose ggrun over LLMKube when License: ggrun is MIT, LLMKube is Apache-2.0; Pricing: Free to use under MIT license; no direct costs involved in usage.; Tags unique to ggrun: cuda, golang, inference-server, llama-cpp; When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs. ### When should I avoid LLMKube? If your infrastructure does not include a Kubernetes cluster, as LLMKube is designed to operate within a Kubernetes environment. If you are looking for a solution that does not support a heterogeneous mix of GPU architectures, LLMKube is specifically designed to work with NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. If you do not require or prefer not to use the specific runtimes supported by LLMKube, such as llama.cpp, vLLM, TGI, and mlx-server. ### When should I avoid ggrun? For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun ### Is LLMKube or ggrun more popular on GitHub? ggrun has more GitHub stars (275 vs 210). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMKube and ggrun open source? Yes - both are open-source projects on GitHub (LLMKube: Apache-2.0, ggrun: MIT). ### Where can I find alternatives to LLMKube or ggrun? GraphCanon lists graph-backed alternatives at [LLMKube alternatives](/tools/defilantech-llmkube/alternatives) and [ggrun alternatives](/tools/raketenkater-ggrun/alternatives) ([LLMKube markdown twin](/tools/defilantech-llmkube/alternatives.md), [ggrun markdown twin](/tools/raketenkater-ggrun/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/defilantech-llmkube-vs-raketenkater-ggrun.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMKube or ggrun? LLMKube: Very active. ggrun: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for LLMKube and ggrun? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMKube trust report](/tools/defilantech-llmkube/trust); [ggrun trust report](/tools/raketenkater-ggrun/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=defilantech-llmkube`](/api/graphcanon/graph?tool=defilantech-llmkube) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_