--- title: "wllama vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ngxson-wllama-vs-turboderp-exllama" tools: ["ngxson-wllama", "turboderp-exllama"] --- # wllama vs exllama *GraphCanon updated Aug 7, 2026* ## Verdict Pick wllama if webAssembly bindings for browser-based inference of llama.cpp; 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. [wllama](https://huggingface.co/spaces/ngxson/wllama) reports 1.2k GitHub stars, 117 forks, and 53 open issues, last pushed Jun 17, 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 [wllama's repository](https://github.com/ngxson/wllama) and [exllama's repository](https://github.com/turboderp/exllama). | | [wllama](/tools/ngxson-wllama.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | WebAssembly binding for llama.cpp - Enabling on-browser LLM inference | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 1,159 | 2,937 | | Forks | 117 | 220 | | Open issues | 53 | 65 | | Language | TypeScript | Python | | Adopt for | WebAssembly bindings for browser-based inference of llama.cpp. | 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 | MIT | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [wllama](/tools/ngxson-wllama.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 51d | 1041d | | Open issues (now) | 53 | 65 | | Full report | [trust report](/tools/ngxson-wllama/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: wllama - **Adopt for:** WebAssembly bindings for browser-based inference of llama.cpp. ## 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 wllama if… - wllama is primarily TypeScript; exllama is Python. - Tags unique to wllama: llama, llamacpp, llm, wasm. - Need browser-based LLM inference directly through WebAssembly. ### Choose exllama if… - exllama is primarily Python; wllama is TypeScript. - 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 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 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 wllama and exllama? wllama: WebAssembly binding for llama.cpp - Enabling on-browser LLM inference. 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 wllama over exllama? Choose wllama over exllama when wllama is primarily TypeScript; exllama is Python; Tags unique to wllama: llama, llamacpp, llm, wasm; Need browser-based LLM inference directly through WebAssembly. ### When should I choose exllama over wllama? Choose exllama over wllama when exllama is primarily Python; wllama is TypeScript; 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 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 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 wllama or exllama more popular on GitHub? exllama has more GitHub stars (2,937 vs 1,159). Stars measure visibility, not whether either tool fits your constraints. ### Are wllama and exllama open source? Yes - both are open-source projects on GitHub (wllama: MIT, exllama: MIT). ### Where can I find alternatives to wllama or exllama? GraphCanon lists graph-backed alternatives at [wllama alternatives](/tools/ngxson-wllama/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([wllama markdown twin](/tools/ngxson-wllama/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/ngxson-wllama-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, wllama or exllama? wllama: Steady. 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 wllama and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [wllama trust report](/tools/ngxson-wllama/trust); [exllama trust report](/tools/turboderp-exllama/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/_