--- title: "gpu-telemetry vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/last9-gpu-telemetry-vs-tensorchord-awesome-llmops" tools: ["last9-gpu-telemetry", "tensorchord-awesome-llmops"] --- # gpu-telemetry vs Awesome-LLMOps *GraphCanon updated Sep 20, 2026* ## 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. [gpu-telemetry](https://last9.io/gpu-observability/) reports 66 GitHub stars, 8 forks, and 5 open issues, last pushed Aug 2, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 1.1k forks, and 317 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [gpu-telemetry's repository](https://github.com/last9/gpu-telemetry) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [gpu-telemetry](/tools/last9-gpu-telemetry.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | GPU Observability with Workload Attribution | An awesome & curated list of best LLMOps tools for developers | | Stars | 66 | 5,941 | | Forks | 8 | 1,058 | | Open issues | 5 | 317 | | Language | Python | Shell | | Adopt for | gpu-telemetry provides comprehensive GPU observability in Kubernetes and Slurm environments by tying hardware metrics to the workload causing them. | 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 | MIT | CC0-1.0 | | Categories | Evaluation & Observability | 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._ | | [gpu-telemetry](/tools/last9-gpu-telemetry.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 39d | 121d | | Open issues (now) | 5 | 317 | | Stars delta | +9 (30d) | +26 (30d) | | Open issues delta | 0 (30d) | +70 (30d) | | Full report | [trust report](/tools/last9-gpu-telemetry/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: gpu-telemetry - **Adopt for:** gpu-telemetry provides comprehensive GPU observability in Kubernetes and Slurm environments by tying hardware metrics to the workload causing them. ## 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 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. ### 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 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 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 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](/tools/last9-gpu-telemetry/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([gpu-telemetry markdown twin](/tools/last9-gpu-telemetry/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/last9-gpu-telemetry-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, 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](/tools/last9-gpu-telemetry/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=last9-gpu-telemetry`](/api/graphcanon/graph?tool=last9-gpu-telemetry) - 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/_