--- title: "LLMKube vs gpustack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/defilantech-llmkube-vs-gpustack-gpustack" tools: ["defilantech-llmkube", "gpustack-gpustack"] --- # LLMKube vs gpustack *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 gpustack if gpustack is a GPU cluster manager that supports high-performance AI model serving and on-demand SSH-accessible GPU instances, suitable for environments requiring robust management. [LLMKube](https://llmkube.com) reports 210 GitHub stars, 33 forks, and 98 open issues, last pushed Sep 17, 2026. [gpustack](https://gpustack.ai) has 5.7k stars, 650 forks, and 700 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [LLMKube's repository](https://github.com/defilantech/LLMKube) and [gpustack's repository](https://github.com/gpustack/gpustack). | | [LLMKube](/tools/defilantech-llmkube.md) | [gpustack](/tools/gpustack-gpustack.md) | | --- | --- | --- | | Tagline | Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets | A GPU cluster manager for high-performance AI model serving and on-demand SSH-accessible GPU instances | | Stars | 210 | 5,710 | | Forks | 33 | 650 | | Open issues | 98 | 700 | | Language | Go | Python | | 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 | gpustack is a GPU cluster manager that supports high-performance AI model serving and on-demand SSH-accessible GPU instances, suitable for environments requiring robust management of GPU resources and support for various | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | gpustack is licensed under the Apache License, Version 2.0, which allows for free use, modification, and distribution of the software, provided that the license terms are adhered to. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMKube](/tools/defilantech-llmkube.md) | [gpustack](/tools/gpustack-gpustack.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 98 | 700 | | Stars delta | +27 (30d) | +256 (30d) | | Open issues delta | +21 (30d) | +27 (30d) | | Full report | [trust report](/tools/defilantech-llmkube/trust.md) | [trust report](/tools/gpustack-gpustack/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: gpustack - **Pricing:** freemium - gpustack is free to use under the Apache License, Version 2.0, but users may incur costs related to the infrastructure and resources used to run the tool. - **Requirements:** Min 4 GB RAM; Requires Docker; gpustack requires Docker for installation and management.; Ensure that the Docker environment is properly set up and that the user has the necessary permissions to run Docker commands. - **Adopt for:** gpustack is a GPU cluster manager that supports high-performance AI model serving and on-demand SSH-accessible GPU instances, suitable for environments requiring robust management of GPU resources and support for various - **License detail:** gpustack is licensed under the Apache License, Version 2.0, which allows for free use, modification, and distribution of the software, provided that the license terms are adhered to. ## Choose when ### Choose LLMKube if… - LLMKube is primarily Go; gpustack is Python. - 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 gpustack if… - gpustack is primarily Python; LLMKube is Go. - Pricing: gpustack is free to use under the Apache License, Version 2.0, but users may incur costs related to the infrastructure and resources used to run the tool.. - Requirements: Min 4 GB RAM; Requires Docker; gpustack requires Docker for installation and management.; Ensure that the Docker environment is properly set up and that the user has the necessary permissions to run Docker commands.. - Tags unique to gpustack: ascend, cuda, deepseek, distributed-inference. - When you need to manage a GPU cluster for high-performance AI model serving and require on-demand SSH-accessible GPU instances. ## 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 gpustack - If your project does not require the specific AI frameworks and technologies supported by gpustack, such as vLLM and SGLang, and you need broader support. - When you are looking for a solution that does not rely on Docker for deployment, as gpustack requires Docker for installation and management. - If you need a tool that provides more extensive customization options beyond what gpustack offers, as it may not be as flexible in terms of configuration. ## Common questions ### What is the difference between LLMKube and gpustack? LLMKube: Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets. gpustack: A GPU cluster manager for high-performance AI model serving and on-demand SSH-accessible GPU instances. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMKube over gpustack? Choose LLMKube over gpustack when LLMKube is primarily Go; gpustack is Python; 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 gpustack over LLMKube? Choose gpustack over LLMKube when gpustack is primarily Python; LLMKube is Go; Pricing: gpustack is free to use under the Apache License, Version 2.0, but users may incur costs related to the infrastructure and resources used to run the tool.; Requirements: Min 4 GB RAM; Requires Docker; gpustack requires Docker for installation and management.; Ensure that the Docker environment is properly set up and that the user has the necessary permissions to run Docker commands.; Tags unique to gpustack: ascend, cuda, deepseek, distributed-inference; When you need to manage a GPU cluster for high-performance AI model serving and require on-demand SSH-accessible GPU instances. ### 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 gpustack? If your project does not require the specific AI frameworks and technologies supported by gpustack, such as vLLM and SGLang, and you need broader support. When you are looking for a solution that does not rely on Docker for deployment, as gpustack requires Docker for installation and management. If you need a tool that provides more extensive customization options beyond what gpustack offers, as it may not be as flexible in terms of configuration. ### Is LLMKube or gpustack more popular on GitHub? gpustack has more GitHub stars (5,710 vs 210). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMKube and gpustack open source? Yes - both are open-source projects on GitHub (LLMKube: Apache-2.0, gpustack: Apache-2.0). ### Where can I find alternatives to LLMKube or gpustack? GraphCanon lists graph-backed alternatives at [LLMKube alternatives](/tools/defilantech-llmkube/alternatives) and [gpustack alternatives](/tools/gpustack-gpustack/alternatives) ([LLMKube markdown twin](/tools/defilantech-llmkube/alternatives.md), [gpustack markdown twin](/tools/gpustack-gpustack/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-gpustack-gpustack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMKube or gpustack? LLMKube: Very active. gpustack: 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 gpustack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMKube trust report](/tools/defilantech-llmkube/trust); [gpustack trust report](/tools/gpustack-gpustack/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/_