--- title: "ggrun vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/raketenkater-ggrun-vs-turboderp-exllama" tools: ["raketenkater-ggrun", "turboderp-exllama"] --- # ggrun vs exllama *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 exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. [ggrun](https://github.com/raketenkater/ggrun) reports 275 GitHub stars, 18 forks, and 4 open issues, last pushed Sep 19, 2026. [exllama](https://github.com/turboderp/exllama) has 2.9k stars, 220 forks, and 65 open issues, last pushed Sep 30, 2023. Figures are from public GitHub metadata via [ggrun's repository](https://github.com/raketenkater/ggrun) and [exllama's repository](https://github.com/turboderp/exllama). | | [ggrun](/tools/raketenkater-ggrun.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 275 | 2,943 | | Forks | 18 | 220 | | Open issues | 4 | 65 | | Language | Go | Python | | 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. | ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. | | 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, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ggrun](/tools/raketenkater-ggrun.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1072d | | Open issues (now) | 4 | 65 | | Stars delta | +11 (30d) | +6 (30d) | | Open issues delta | +3 (30d) | 0 (30d) | | Full report | [trust report](/tools/raketenkater-ggrun/trust.md) | [trust report](/tools/turboderp-exllama/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: exllama - **Adopt for:** ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. ## Choose when ### Choose ggrun if… - ggrun is primarily Go; exllama is Python. - 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 exllama if… - exllama is primarily Python; ggrun is Go. - Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu. - Also covers LLM Frameworks. - exllama ships Docker support for self-hosted deployment. - - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ## 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 exllama - - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts). ## Common questions ### What is the difference between ggrun and exllama? ggrun: Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server. exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories. ### When should I choose ggrun over exllama? Choose ggrun over exllama when ggrun is primarily Go; exllama is Python; 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 exllama over ggrun? Choose exllama over ggrun when exllama is primarily Python; ggrun is Go; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; exllama ships Docker support for self-hosted deployment; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ### 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 exllama? - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts). ### Is ggrun or exllama more popular on GitHub? exllama has more GitHub stars (2,943 vs 275). Stars measure visibility, not whether either tool fits your constraints. ### Are ggrun and exllama open source? Yes - both are open-source projects on GitHub (ggrun: MIT, exllama: MIT). ### Where can I find alternatives to ggrun or exllama? GraphCanon lists graph-backed alternatives at [ggrun alternatives](/tools/raketenkater-ggrun/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([ggrun markdown twin](/tools/raketenkater-ggrun/alternatives.md), [exllama markdown twin](/tools/turboderp-exllama/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-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ggrun or exllama? ggrun: Very active. exllama: Dormant. 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 exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ggrun trust report](/tools/raketenkater-ggrun/trust); [exllama trust report](/tools/turboderp-exllama/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/_