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mozilla-ai
llamafile
C++2023–2026
mozilla-ai / llamafile

llamafile

Distribute and run LLMs with a single file.

82Distinct people
112Mentions
3Explicit recommenders
26.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Distribute and run LLMs with a single file.

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

Handy is an awesome project, highly recommended - many of our engineers and PMs use it! CJ, Handy's creator, recently joined us as a Builder in Residence at Mozilla.ai. So for…

JohnPDickerson used a recommendation phrase. 3 indexed comments match. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 6:36:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year112 dated mentions

Observed mentions: 2023-11-29 19:29:49 UTC to 2026-10-27 20:17:14 UTC.

02040602023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 26 mentions; 2 submissions, 24 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 62 mentions; 5 submissions, 57 comments20242025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 19 mentions; 3 submissions, 16 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC26224
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC62557
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC19316
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC505

From the project README

An excerpt from the repository, fetched Nov 12, 2026.

Read the project’s introduction
llamafile llamafile lets you distribute and run LLMs with a single file. llamafile is a Mozilla Builders project (see its announcement blog post), now revamped by Mozilla.ai. Our goal is to make open LLMs much more accessible to both developers and end users. We're doing that by combining llama.cpp with Cosmopolitan Libc into one framework that collapses all the complexity of LLMs down to a single-file executable (called a "llamafile") that runs locally on most operating systems and CPU architectures, with no installation. llamafile also includes whisperfile, a single-file speech-to-text tool built on whisper.cpp and the same Cosmopolitan packaging. It supports transcription and translation of audio files across all the same platforms, with no installation required. v0.10.* llamafile versions starting from 0.10.0 use a new build system, aimed at keeping our code more easily aligned with the latest versions of llama.cpp. This means they support more recent models and functionalities, but at the same time they might be missing some of the features you were accustomed to (check out this doc for a high-level description of what has been done). If you liked the "classic experience" more, you will always be able to access the previous versions from our releases page. Our pre-built llamafiles always show which version of the server they have been bundled with (0.9.* example, 0.10.* example), so you will always know which version of the software you are downloading. We want to hear from you! Whether you are a new user or a long-time fan, please share what you find most valuable about llamafile and what would make it more useful for you. Read more via the blog and add your voice to the discussion here. Quick Start Download and run your first llamafile in minutes: # Download an example model (Qwen3.5 0.8B) curl -LO https://huggingface.co/mozilla-ai/llamafile_0.10/resolve/main/Qwen3.5-0.8B-Q8_0.llamafile # Make it executable (macOS/Linux/BSD) chmod +x Qwen3.5-0.8B-Q8_0.llamafile # Run it ./Qwen3.5-0.8B-Q8_0.llamafile We chose this model because that's the smallest one we have built a llamafile for, so most likely to work out-of-the-box for you. If you have powerful hardware and/or GPUs, feel free to choose larger and more expressive models which should provide more accurate responses. Windows users: Rename the file to add .exe extension before running. Note - Only executables under 4GB can run on Windows, so any llamafile above 4GB won't work. Download the llamafile binary and run it with any external weights/models(GGUF). Documentation Check the full documentation at docs.mozilla.ai/llamafile, or directly jump into one of the following subsections: Quickstart Pre-built llamafiles Running a llamafile Creating llamafiles Source installation Technical details Supported Systems Troubleshooting Whisperfile Licensing While the llamafile project is Apache 2.0-licensed, our changes to llama.cpp and whisper.cpp are licensed under MIT (just like the projects themselves) so as to remain compatible and upstreamable in the future, should that be desired. The llamafile logo on this page was generated with the assistance of DALL·E 3.
Read the full README on GitHub

What Hacker News says

62 sources
I'm encouraged that they're actively exploring this and not shying away from experiments. It seems clear to me that there are areas of Mozilla[0],[1] pushing closer and closer to great local AI integrations doing the kinds of things that I, a browser user, find useful. I went to some of the articles on the hn frontpage and had questions (and followups!) that begat reasonable starting points for further learning. Hopefully they continue to iterate on this with better integration (for instance, moving to a toolbar icon instead of persistent badge on every page) and then make it ~truly privacy respecting by moving locally. [0] https://github.com/Mozilla-Ocho/llamafile [1] https://github.com/mozilla/translations
In Orbit by Mozilla
You might also try https://github.com/Mozilla-Ocho/llamafile , which may have better CPU-only performance than ollama. It does require you to grab .gguf files yourself (unless you use one of their prebuilts in which case it comes with the binary!), but with that done it's really easy to use and has decent performance. For reference, this is how I run it: $ cat ~/.config/systemd/user/llamafile@.service [Unit] Description=llamafile with arbitrary model After=network.target [Service] Type=simple WorkingDirectory=%h/llms/ ExecStart=sh -c "%h/.local/bin/llamafile -m %h/llamafile-models/%i.gguf --server --host '::' --port 8081 --nobrowser --log-disable" [Install] WantedBy=default.target And then systemctl --user start llamafile@whatevermodel but you can just run that ExecStart command directly and it works.
In Llama.cpp guide – Running LLMs locally on any hardware, from scratch
I wrote https://github.com/Mozilla-Ocho/llamafile/blob/main/llamafil... and it does a reasonable job highlighting without breaking for all the .tex files I could find on my hard drive. My goal is to hopefully cover 99.9% of real world usage, since that'll likely cover everything an LLM might output. Esoteric syntax also usually isn't a problem, so long as it doesn't cause strings and comments to extend forever, eclipsing the rest of the source code in a file.
In Weird Lexical Syntax
yjftsjthsd-h·Oct 11, 2024Recommendation phrase
If your goal is > I want to peel back the layers of the onion and other gluey-mess to gain insight into these models. Then this is great. If your goal is > Run and explore Llama models locally with minimal dependencies on CPU then I recommend https://github.com/Mozilla-Ocho/llamafile which ships as a single file with no dependencies and runs on CPU with great performance. Like, such great performance that I've mostly given up on GPU for LLMs. It was a game changer.
In Run Llama locally with only PyTorch on CPU
Local models are definitely not as smart as ChatGPT but you can get pretty close! I'd consider them to be about a year behind in terms of performance compared to hosted models, which is not surprising considering the resource constraints. I've found that you can get faster performance by choosing a smaller model and/or by using a smaller quantization. You can use other models with llamafile as well. They have some prebuilt ones: https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file... You can also search for other llamafiles for other models on HuggingFace by using the llamafile tag. https://huggingface.co/models?library=llamafile&sort=trendin... And you can download model weights directly and use them by providing an -m flag to llamafile but that's getting a bit less straightforward. https://github.com/Mozilla-Ocho/llamafile?tab=r
Read the rest
In Forget ChatGPT: why researchers now run small AIs on their laptops
Make yourself comfortable with https://blogs.oracle.com/database/post/freedom-to-build-anno... https://gist.github.com/rssnyder/51e3cfedd730e7dd5f4a816143b... https://www.reddit.com/r/oraclecloud/ or any other offer. Deploy some minimal Linux on them, or use what's offered. Plus optionally, if you don't want to instantly start coding from first principles/scratch, make use of established and excellent solutions, like https://future.mozilla.org/builders/news_insights/introducin... https://ai-guide.future.mozilla.org/content/running-llms-loc... https://github.com/mozilla-Ocho/llamafile https://justine.lol/matmul/ and parallelize them with https://github.com/b4rtaz/distributed-llama Obviously this needs some knowledge of the command line, so get a good terminal emulator like https://iterm2.com/ Mend, bend, rend that stuff and see what works
Read the rest
how and why, and what not. Edit: Optionally, if you really want to go low-level, with some debugger like https://justine.lol/blinkenlights/ for 'toy-installations' of smallest models. 'Toy' because that doesn't fully support the CPU-instructions which are used in production. Could still help conceptually.
In Ask HN: How can I experiment with LLMs with a old machine?
For anyone who hasn't tried local models because they think it's too complicated or their computer can't handle it, download a single llamafile and try it out in just moments. https://future.mozilla.org/builders/news_insights/introducin... https://github.com/Mozilla-Ocho/llamafile They even have whisperfiles now, which is the same thing but for whisper.cpp, aka real-time voice transcription. You can also take this a step further and use this exact setup for a local-only co-pilot style code autocomplete and chat using Twinny. I use this every day. It's free, private, and offline. https://github.com/twinnydotdev/twinny Local LLMs are the only future worth living in.
In Forget ChatGPT: why researchers now run small AIs on their laptops
To run text-to-speech on my laptop, I've been using Justine Tunney's downloadable single executable Whisper file. I use it transcribe audio then copy into an LLM to get notes on whatever it is. Helps me decide to watch or listen to something and saves a bunch of time. Her tweet: https://x.com/JustineTunney/status/1825551821857010143 Instructions from Simon Willison: https://simonwillison.net/2024/Aug/19/whisperfile/ Command line options: https://github.com/Mozilla-Ocho/llamafile/issues/544#issueco...
In Speech Dictation Mode for Emacs

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