--- title: "gpustack vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gpustack-gpustack-vs-tensorchord-awesome-llmops" tools: ["gpustack-gpustack", "tensorchord-awesome-llmops"] --- # gpustack vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick gpustack if gpustack is a Python-based tool for managing GPU clusters focused on efficient AI model inference and on-demand SSH-accessible GPU instances; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. [gpustack](https://gpustack.ai) reports 5.5k GitHub stars, 609 forks, and 673 open issues, last pushed Aug 7, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [gpustack's repository](https://github.com/gpustack/gpustack) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [gpustack](/tools/gpustack-gpustack.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A GPU cluster manager for high-performance AI model serving and on-demand SSH-accessible GPU instances | An awesome & curated list of best LLMOps tools for developers | | Stars | 5,454 | 5,915 | | Forks | 609 | 993 | | Open issues | 673 | 247 | | Language | Python | Shell | | Adopt for | gpustack is a Python-based tool for managing GPU clusters focused on efficient AI model inference and on-demand SSH-accessible GPU instances. | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | Inference & Serving | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpustack](/tools/gpustack-gpustack.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 91d | | Open issues (now) | 673 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/gpustack-gpustack/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: gpustack - **Pricing:** freemium - **Requirements:** Requires Docker - **Adopt for:** gpustack is a Python-based tool for managing GPU clusters focused on efficient AI model inference and on-demand SSH-accessible GPU instances. - **License detail:** Apache-2.0 ## Decision facts: Awesome-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Choose when ### Choose gpustack if… - gpustack is primarily Python; Awesome-LLMOps is Shell. - License: gpustack is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Requirements: Requires Docker. - Tags unique to gpustack: ascend, cuda, deepseek, distributed-inference. - When you need to manage multiple GPUs for high-performance inference tasks with models like vLLM or SGLang. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; gpustack is Python. - License: Awesome-LLMOps is CC0-1.0, gpustack is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use gpustack - If your deployment constraints do not permit the use of Docker containers and there is a need for bare-metal deployments without containerized solutions. - When the tool-specific focus on certain models like vLLM or SGLang does not align with the model ecosystem preferred by your team. ## When NOT to use Awesome-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## Common questions ### What is the difference between gpustack and Awesome-LLMOps? gpustack: A GPU cluster manager for high-performance AI model serving and on-demand SSH-accessible GPU instances. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose gpustack over Awesome-LLMOps? Choose gpustack over Awesome-LLMOps when gpustack is primarily Python; Awesome-LLMOps is Shell; License: gpustack is Apache-2.0, Awesome-LLMOps is CC0-1.0; Requirements: Requires Docker; Tags unique to gpustack: ascend, cuda, deepseek, distributed-inference; When you need to manage multiple GPUs for high-performance inference tasks with models like vLLM or SGLang. ### When should I choose Awesome-LLMOps over gpustack? Choose Awesome-LLMOps over gpustack when Awesome-LLMOps is primarily Shell; gpustack is Python; License: Awesome-LLMOps is CC0-1.0, gpustack is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid gpustack? If your deployment constraints do not permit the use of Docker containers and there is a need for bare-metal deployments without containerized solutions. When the tool-specific focus on certain models like vLLM or SGLang does not align with the model ecosystem preferred by your team. ### When should I avoid Awesome-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### Is gpustack or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 5,454). Stars measure visibility, not whether either tool fits your constraints. ### Are gpustack and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (gpustack: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to gpustack or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [gpustack alternatives](/tools/gpustack-gpustack/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([gpustack markdown twin](/tools/gpustack-gpustack/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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/gpustack-gpustack-vs-tensorchord-awesome-llmops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gpustack or Awesome-LLMOps? gpustack: Very active. Awesome-LLMOps: Slowing. 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 gpustack and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gpustack trust report](/tools/gpustack-gpustack/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gpustack-gpustack`](/api/graphcanon/graph?tool=gpustack-gpustack) - 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/_