Comparison
gpu-telemetry vs Awesome-LLMOps
Verdict
Pick gpu-telemetry if gpu-telemetry provides comprehensive GPU observability in Kubernetes and Slurm environments by tying hardware metrics to the workload causing them; 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 · gpu-telemetry alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
10views this month
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Trust & integrity
| Signal | gpu-telemetry | Awesome-LLMOps |
|---|---|---|
| Maintenance | Steady (39d since push) As of Sep 11, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 11, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- gpu-telemetry
- GPU Observability with Workload Attribution
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- gpu-telemetry
- 66
- Awesome-LLMOps
- 5.9k
Forks
- gpu-telemetry
- 8
- Awesome-LLMOps
- 1.1k
Open issues
- gpu-telemetry
- 5
- Awesome-LLMOps
- 317
Language
- gpu-telemetry
- Python
- Awesome-LLMOps
- Shell
Adopt for
- gpu-telemetry
- gpu-telemetry provides comprehensive GPU observability in Kubernetes and Slurm environments by tying hardware metrics to the workload causing them.
- 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
- gpu-telemetry
- -
- Awesome-LLMOps
- -
Runtime
- gpu-telemetry
- -
- Awesome-LLMOps
- -
License
- gpu-telemetry
- MIT
- Awesome-LLMOps
- CC0-1.0
Last pushed
- gpu-telemetry
- Aug 2, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- gpu-telemetry
- Evaluation & Observability
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- gpu-telemetry
- Steady (60%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- gpu-telemetry
- 39d
- Awesome-LLMOps
- 121d
Open issues (now)
- gpu-telemetry
- 5
- Awesome-LLMOps
- 317
Stars delta
- gpu-telemetry
- +9 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- gpu-telemetry
- 0 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- gpu-telemetry
- Trust report
- Awesome-LLMOps
- Trust report
Choose gpu-telemetry if…
- gpu-telemetry is primarily Python; Awesome-LLMOps is Shell.
- License: gpu-telemetry is MIT, Awesome-LLMOps is CC0-1.0.
- Tags unique to gpu-telemetry: amd, gpu-monitoring, intel-gaudi-base-operator, kubernetes.
- When monitoring NVIDIA, AMD, or Intel Gaudi GPUs in Kubernetes clusters.
When NOT to use gpu-telemetry
- If your infrastructure is not based on Kubernetes or Slurm.
- When you prefer tools that do not require per-node OTLP agents.
- For environments without support for NVIDIA, AMD, or Intel Gaudi GPUs.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; gpu-telemetry is Python.
- License: Awesome-LLMOps is CC0-1.0, gpu-telemetry is MIT.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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 (last9/gpu-telemetry) · observed Sep 20, 2026
- GitHub forks (last9/gpu-telemetry) · observed Sep 20, 2026
- Last push (last9/gpu-telemetry) · observed Aug 2, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: gpu-telemetry 66 · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between gpu-telemetry and Awesome-LLMOps?
- gpu-telemetry: GPU Observability with Workload Attribution. 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 gpu-telemetry over Awesome-LLMOps?
- Choose gpu-telemetry over Awesome-LLMOps when gpu-telemetry is primarily Python; Awesome-LLMOps is Shell; License: gpu-telemetry is MIT, Awesome-LLMOps is CC0-1.0; Tags unique to gpu-telemetry: amd, gpu-monitoring, intel-gaudi-base-operator, kubernetes; When monitoring NVIDIA, AMD, or Intel Gaudi GPUs in Kubernetes clusters.
- When should I choose Awesome-LLMOps over gpu-telemetry?
- Choose Awesome-LLMOps over gpu-telemetry when Awesome-LLMOps is primarily Shell; gpu-telemetry is Python; License: Awesome-LLMOps is CC0-1.0, gpu-telemetry is MIT; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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 gpu-telemetry?
- If your infrastructure is not based on Kubernetes or Slurm. When you prefer tools that do not require per-node OTLP agents. For environments without support for NVIDIA, AMD, or Intel Gaudi GPUs.
- 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 gpu-telemetry or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 66). Stars measure visibility, not whether either tool fits your constraints.
- Are gpu-telemetry and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (gpu-telemetry: MIT, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to gpu-telemetry or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at gpu-telemetry alternatives and Awesome-LLMOps alternatives (gpu-telemetry 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, gpu-telemetry or Awesome-LLMOps?
- gpu-telemetry: Steady. 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 gpu-telemetry and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpu-telemetry trust report; Awesome-LLMOps trust report.