Magi LLM and Exllama: A Powerful Combination

Magi LLM is a versatile language model that has gained popularity among developers and researchers. It supports Exllama as a backend, offering enhanced capabilities for text generation and synthesis.

Exllama, available at, is a powerful tool that comes with a basic WebUI. This integration allows users to leverage both Exllama and the latest version of Llamacpp for blazing-fast text synthesis.

One of the key advantages of using Exllama is its speed. Users have reported that it significantly improves the generation process, enabling them to achieve higher token-per-second rates compared to other methods.

Curious to learn more about Exllama's performance, a Reddit user shared their positive experience and asked if it could be used with other tools. You can find the discussion thread at Many users agreed that Exllama was impressive in terms of speed and ease of installation.

Although Exllama offers remarkable performance, it's important to note that it has encountered some issues. Developers are actively working on fixing these problems and implementing improvements to ensure a smoother user experience.

If you're interested in using Exllama, you can find a pull request for integrating it with Oobabooga's text generation web UI at The pull request adds Exllama support, although some samplers are still missing. Hopefully, this integration will be merged soon, enhancing the overall functionality of the web UI.

Another tool worth mentioning is Kobold, available at Kobold works alongside Exllama and provides additional features. To take full advantage of both tools, you can visit

While Exllama's compatibility with different models is not explicitly mentioned, it has shown promising results with GPT-Q. As for multiple GPUs, it is advisable to refer to the documentation or the respective GitHub repositories for the most up-to-date information on Exllama's capabilities.

Tags: Magi LLM, Exllama, text generation, synthesis, language model, backend, WebUI, Llamacpp, speed, installation, performance, pull request

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