--- title: "wllama vs ggrun" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ngxson-wllama-vs-raketenkater-ggrun" tools: ["ngxson-wllama", "raketenkater-ggrun"] --- # wllama vs ggrun *GraphCanon updated Aug 13, 2026* ## Verdict Pick wllama if webAssembly bindings for browser-based inference of llama.cpp; 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. [wllama](https://huggingface.co/spaces/ngxson/wllama) reports 1.2k GitHub stars, 117 forks, and 53 open issues, last pushed Jun 17, 2026. [ggrun](https://github.com/raketenkater/ggrun) has 264 stars, 14 forks, and 1 open issues, last pushed Aug 11, 2026. Figures are from public GitHub metadata via [wllama's repository](https://github.com/ngxson/wllama) and [ggrun's repository](https://github.com/raketenkater/ggrun). | | [wllama](/tools/ngxson-wllama.md) | [ggrun](/tools/raketenkater-ggrun.md) | | --- | --- | --- | | Tagline | WebAssembly binding for llama.cpp - Enabling on-browser LLM inference | Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server | | Stars | 1,159 | 264 | | Forks | 117 | 14 | | Open issues | 53 | 1 | | Language | TypeScript | Go | | Adopt for | WebAssembly bindings for browser-based inference of llama.cpp. | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [wllama](/tools/ngxson-wllama.md) | [ggrun](/tools/raketenkater-ggrun.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 51d | 1d | | Open issues (now) | 53 | 1 | | Full report | [trust report](/tools/ngxson-wllama/trust.md) | [trust report](/tools/raketenkater-ggrun/trust.md) | ## Decision facts: wllama - **Adopt for:** WebAssembly bindings for browser-based inference of llama.cpp. ## 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. ## Choose when ### Choose wllama if… - wllama is primarily TypeScript; ggrun is Go. - Tags unique to wllama: llama, llamacpp, wasm, webassembly. - Need browser-based LLM inference directly through WebAssembly. ### Choose ggrun if… - ggrun is primarily Go; wllama is TypeScript. - 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 NOT to use wllama - Require direct native execution speed benefits unavailable in a WebAssembly context. - Developing server-side applications without the need for client-side inference capabilities. ## 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 ## Common questions ### What is the difference between wllama and ggrun? wllama: WebAssembly binding for llama.cpp - Enabling on-browser LLM inference. ggrun: Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server. See the comparison table for live GitHub stats and shared categories. ### When should I choose wllama over ggrun? Choose wllama over ggrun when wllama is primarily TypeScript; ggrun is Go; Tags unique to wllama: llama, llamacpp, wasm, webassembly; Need browser-based LLM inference directly through WebAssembly. ### When should I choose ggrun over wllama? Choose ggrun over wllama when ggrun is primarily Go; wllama is TypeScript; 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 avoid wllama? Require direct native execution speed benefits unavailable in a WebAssembly context. Developing server-side applications without the need for client-side inference capabilities. ### 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 ### Is wllama or ggrun more popular on GitHub? wllama has more GitHub stars (1,159 vs 264). Stars measure visibility, not whether either tool fits your constraints. ### Are wllama and ggrun open source? Yes - both are open-source projects on GitHub (wllama: MIT, ggrun: MIT). ### Where can I find alternatives to wllama or ggrun? GraphCanon lists graph-backed alternatives at [wllama alternatives](/tools/ngxson-wllama/alternatives) and [ggrun alternatives](/tools/raketenkater-ggrun/alternatives) ([wllama markdown twin](/tools/ngxson-wllama/alternatives.md), [ggrun markdown twin](/tools/raketenkater-ggrun/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/ngxson-wllama-vs-raketenkater-ggrun.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, wllama or ggrun? wllama: Steady. ggrun: Very active. 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 wllama and ggrun? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [wllama trust report](/tools/ngxson-wllama/trust); [ggrun trust report](/tools/raketenkater-ggrun/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ngxson-wllama`](/api/graphcanon/graph?tool=ngxson-wllama) - 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/_