--- title: "shimmy vs wllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/michael-a-kuykendall-shimmy-vs-ngxson-wllama" tools: ["michael-a-kuykendall-shimmy", "ngxson-wllama"] --- # shimmy vs wllama *GraphCanon updated Sep 20, 2026* ## Verdict Pick shimmy if shimmy is a Rust-based inference engine that supports WebGPU for local machine learning model inference, compatible with the OpenAI API and GGUF models, offering a command-line interface; pick wllama if webAssembly bindings for browser-based inference of llama.cpp. [shimmy](https://github.com/Michael-A-Kuykendall/shimmy) reports 5.9k GitHub stars, 576 forks, and 10 open issues, last pushed Aug 30, 2026. [wllama](https://huggingface.co/spaces/ngxson/wllama) has 1.2k stars, 121 forks, and 47 open issues, last pushed Sep 6, 2026. Figures are from public GitHub metadata via [shimmy's repository](https://github.com/Michael-A-Kuykendall/shimmy) and [wllama's repository](https://github.com/ngxson/wllama). | | [shimmy](/tools/michael-a-kuykendall-shimmy.md) | [wllama](/tools/ngxson-wllama.md) | | --- | --- | --- | | Tagline | Pure-Rust WebGPU inference engine, OpenAI-API compatible, GGUF native | WebAssembly binding for llama.cpp - Enabling on-browser LLM inference | | Stars | 5,890 | 1,196 | | Forks | 576 | 121 | | Open issues | 10 | 47 | | Language | Rust | TypeScript | | Adopt for | Shimmy is a Rust-based inference engine that supports WebGPU for local machine learning model inference, compatible with the OpenAI API and GGUF models, offering a command-line interface. | WebAssembly bindings for browser-based inference of llama.cpp. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Developer Tools, Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [shimmy](/tools/michael-a-kuykendall-shimmy.md) | [wllama](/tools/ngxson-wllama.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 18d | 0d | | Open issues (now) | 10 | 47 | | Stars delta | +193 (30d) | +37 (30d) | | Open issues delta | -1 (30d) | -6 (30d) | | Full report | [trust report](/tools/michael-a-kuykendall-shimmy/trust.md) | [trust report](/tools/ngxson-wllama/trust.md) | ## Decision facts: shimmy - **Requirements:** Shimmy requires a GPU for optimal performance.; The tool is designed to be used with GGUF models and is compatible with the OpenAI API. - **Adopt for:** Shimmy is a Rust-based inference engine that supports WebGPU for local machine learning model inference, compatible with the OpenAI API and GGUF models, offering a command-line interface. - **License detail:** Apache-2.0 ## Decision facts: wllama - **Adopt for:** WebAssembly bindings for browser-based inference of llama.cpp. ## Choose when ### Choose shimmy if… - shimmy is primarily Rust; wllama is TypeScript. - License: shimmy is Apache-2.0, wllama is MIT. - Requirements: Shimmy requires a GPU for optimal performance.; The tool is designed to be used with GGUF models and is compatible with the OpenAI API.. - Tags unique to shimmy: api-server, command-line-tool, gguf, huggingface. - Also covers Developer Tools. - shimmy ships Docker support for self-hosted deployment. - When you need a Rust-based inference engine that supports WebGPU for running machine learning models locally. ### Choose wllama if… - wllama is primarily TypeScript; shimmy is Rust. - License: wllama is MIT, shimmy is Apache-2.0. - Tags unique to wllama: llama, llamacpp, llm, wasm. - Need browser-based LLM inference directly through WebAssembly. ## When NOT to use shimmy - If you require a solution that supports languages other than Rust or frameworks that are not WebGPU-based. - When you need a tool that integrates with Python or llama.cpp, as Shimmy explicitly avoids these dependencies. - If you are looking for a multi-component system; Shimmy is designed as a single binary for simplicity. - When you need a paid product with additional features or support, as Shimmy is committed to being free forever. ## 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. ## Common questions ### What is the difference between shimmy and wllama? shimmy: Pure-Rust WebGPU inference engine, OpenAI-API compatible, GGUF native. wllama: WebAssembly binding for llama.cpp - Enabling on-browser LLM inference. See the comparison table for live GitHub stats and shared categories. ### When should I choose shimmy over wllama? Choose shimmy over wllama when shimmy is primarily Rust; wllama is TypeScript; License: shimmy is Apache-2.0, wllama is MIT; Requirements: Shimmy requires a GPU for optimal performance.; The tool is designed to be used with GGUF models and is compatible with the OpenAI API.; Tags unique to shimmy: api-server, command-line-tool, gguf, huggingface; Also covers Developer Tools; shimmy ships Docker support for self-hosted deployment; When you need a Rust-based inference engine that supports WebGPU for running machine learning models locally. ### When should I choose wllama over shimmy? Choose wllama over shimmy when wllama is primarily TypeScript; shimmy is Rust; License: wllama is MIT, shimmy is Apache-2.0; Tags unique to wllama: llama, llamacpp, llm, wasm; Need browser-based LLM inference directly through WebAssembly. ### When should I avoid shimmy? If you require a solution that supports languages other than Rust or frameworks that are not WebGPU-based. When you need a tool that integrates with Python or llama.cpp, as Shimmy explicitly avoids these dependencies. If you are looking for a multi-component system; Shimmy is designed as a single binary for simplicity. When you need a paid product with additional features or support, as Shimmy is committed to being free forever. ### 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. ### Is shimmy or wllama more popular on GitHub? shimmy has more GitHub stars (5,890 vs 1,196). Stars measure visibility, not whether either tool fits your constraints. ### Are shimmy and wllama open source? Yes - both are open-source projects on GitHub (shimmy: Apache-2.0, wllama: MIT). ### Where can I find alternatives to shimmy or wllama? GraphCanon lists graph-backed alternatives at [shimmy alternatives](/tools/michael-a-kuykendall-shimmy/alternatives) and [wllama alternatives](/tools/ngxson-wllama/alternatives) ([shimmy markdown twin](/tools/michael-a-kuykendall-shimmy/alternatives.md), [wllama markdown twin](/tools/ngxson-wllama/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/michael-a-kuykendall-shimmy-vs-ngxson-wllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, shimmy or wllama? shimmy: Active. wllama: 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 shimmy and wllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [shimmy trust report](/tools/michael-a-kuykendall-shimmy/trust); [wllama trust report](/tools/ngxson-wllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=michael-a-kuykendall-shimmy`](/api/graphcanon/graph?tool=michael-a-kuykendall-shimmy) - 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/_