--- title: "ggrun vs Server" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/raketenkater-ggrun-vs-rubixml-server" tools: ["raketenkater-ggrun", "rubixml-server"] --- # ggrun vs Server *GraphCanon updated Sep 20, 2026* ## Verdict Pick ggrun if ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities; pick Server if server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP. [ggrun](https://github.com/raketenkater/ggrun) reports 275 GitHub stars, 18 forks, and 4 open issues, last pushed Sep 19, 2026. [Server](https://rubixml.github.io/ML) has 63 stars, 13 forks, and 1 open issues, last pushed Mar 3, 2026. Figures are from public GitHub metadata via [ggrun's repository](https://github.com/raketenkater/ggrun) and [Server's repository](https://github.com/RubixML/Server). | | [ggrun](/tools/raketenkater-ggrun.md) | [Server](/tools/rubixml-server.md) | | --- | --- | --- | | Tagline | Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server | Standalone inference server for Rubix ML estimators. | | Stars | 275 | 63 | | Forks | 18 | 13 | | Open issues | 4 | 1 | | Language | Go | PHP | | Adopt for | ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. | Server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP. | | Persona | - | - | | Runtime | - | - | | License | MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved. | MIT | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ggrun](/tools/raketenkater-ggrun.md) | [Server](/tools/rubixml-server.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 201d | | Open issues (now) | 4 | 1 | | Stars delta | +11 (30d) | 0 (30d) | | Open issues delta | +3 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/raketenkater-ggrun/trust.md) | [trust report](/tools/rubixml-server/trust.md) | ## Decision facts: ggrun - **Pricing:** freemium - Free to use under MIT license; no direct costs involved in usage. - **Adopt for:** ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. - **License detail:** MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved. ## Decision facts: Server - **Adopt for:** Server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP. ## Choose when ### Choose ggrun if… - ggrun is primarily Go; Server is PHP. - Pricing: Free to use under MIT license; no direct costs involved in usage.. - Tags unique to ggrun: cuda, gguf, golang, inference-server. - When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs ### Choose Server if… - Server is primarily PHP; ggrun is Go. - Tags unique to Server: api, http-server, inference-engine, infrastructure. - When you are working with machine learning models trained in the Rubix ML framework and need to deploy them via a PHP-based infrastructure. ## When NOT to use ggrun - For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity - When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun ## When NOT to use Server - Avoid using if your primary technology stack is not based on PHP, as it would necessitate integration with a non-native language environment, increasing complexity. - Do not use this tool for large-scale deployments requiring high throughput and low latency typical of more robust languages like Python or Rust. ## Common questions ### What is the difference between ggrun and Server? ggrun: Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server. Server: Standalone inference server for Rubix ML estimators.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ggrun over Server? Choose ggrun over Server when ggrun is primarily Go; Server is PHP; Pricing: Free to use under MIT license; no direct costs involved in usage.; Tags unique to ggrun: cuda, gguf, golang, inference-server; When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs. ### When should I choose Server over ggrun? Choose Server over ggrun when Server is primarily PHP; ggrun is Go; Tags unique to Server: api, http-server, inference-engine, infrastructure; When you are working with machine learning models trained in the Rubix ML framework and need to deploy them via a PHP-based infrastructure. ### When should I avoid ggrun? For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun ### When should I avoid Server? Avoid using if your primary technology stack is not based on PHP, as it would necessitate integration with a non-native language environment, increasing complexity. Do not use this tool for large-scale deployments requiring high throughput and low latency typical of more robust languages like Python or Rust. ### Is ggrun or Server more popular on GitHub? ggrun has more GitHub stars (275 vs 63). Stars measure visibility, not whether either tool fits your constraints. ### Are ggrun and Server open source? Yes - both are open-source projects on GitHub (ggrun: MIT, Server: MIT). ### Where can I find alternatives to ggrun or Server? GraphCanon lists graph-backed alternatives at [ggrun alternatives](/tools/raketenkater-ggrun/alternatives) and [Server alternatives](/tools/rubixml-server/alternatives) ([ggrun markdown twin](/tools/raketenkater-ggrun/alternatives.md), [Server markdown twin](/tools/rubixml-server/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/raketenkater-ggrun-vs-rubixml-server.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ggrun or Server? ggrun: Very active. Server: 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 ggrun and Server? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ggrun trust report](/tools/raketenkater-ggrun/trust); [Server trust report](/tools/rubixml-server/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=raketenkater-ggrun`](/api/graphcanon/graph?tool=raketenkater-ggrun) - 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/_