Comparison
gpustack vs Awesome-LLMOps
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.
Markdown twin · gpustack alternatives · Awesome-LLMOps alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | gpustack | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1w · github_public_v1 | Steady (60d since push) As of 1mo · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 1mo · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- gpustack
- 5.5k
- Awesome-LLMOps
- 5.9k
Forks
- gpustack
- 609
- Awesome-LLMOps
- 924
Open issues
- gpustack
- 673
- Awesome-LLMOps
- 181
Language
- gpustack
- Python
- Awesome-LLMOps
- Shell
Adopt for
- gpustack
- 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
- 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
- gpustack
- -
- Awesome-LLMOps
- -
Runtime
- gpustack
- -
- Awesome-LLMOps
- -
License
- gpustack
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- gpustack
- Aug 7, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- gpustack
- Inference & Serving
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- gpustack
- Very active (96%)
- Awesome-LLMOps
- Steady (60%)
Days since push
- gpustack
- 0d
- Awesome-LLMOps
- 60d
Open issues (now)
- gpustack
- 673
- Awesome-LLMOps
- 181
Full report
- gpustack
- Trust report
- Awesome-LLMOps
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (gpustack/gpustack) · observed Aug 7, 2026
- GitHub forks (gpustack/gpustack) · observed Aug 7, 2026
- Last push (gpustack/gpustack) · observed Aug 7, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Jul 21, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Jul 21, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Jul 21, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: gpustack 5.5k · Awesome-LLMOps 5.9k (synced Aug 7, 2026).
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 (5,887 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 and Awesome-LLMOps alternatives (gpustack markdown twin, Awesome-LLMOps markdown twin), 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 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: Steady. 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; Awesome-LLMOps trust report.