llamafile

tom@tomrochette.com (Tom Rochette) — Wed, 07 Oct 2026 00:00:00 +0000

llamafile is Mozilla’s single-file LLM distribution format: it folds llama.cpp and Cosmopolitan Libc into one executable so a weights file and its runtime become a single binary that runs on six operating systems with no installation.

llamafile attacks distribution rather than serving: where Ollama, the family’s incumbent, gives your machine a model registry and a daemon, llamafile gives one person a file they can hand to another person, which is why it remains the only member here whose unit of delivery is an email attachment.

What it is
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A Mozilla Builders project launched November 2023, built by Justine Tunney (the Cosmopolitan Libc author) and now revamped by Mozilla.ai, licensed Apache-2.0 with its llama.cpp changes MIT so they can move upstream (26,190 stars, pushed 2026-10-07, as of 2026-10-07). You point it at a GGUF weights file and get one cross-platform binary containing the weights, the inference engine, and a web UI, runnable on macOS, Linux, Windows, and the BSDs across six OS targets, on CPU or GPU, with no install step. The same packaging ships whisperfile, a single-file speech-to-text and translation tool on whisper.cpp. Release 0.10.6 (September 2026) moved to a new build system on the 0.10 line, and the project publishes pre-built llamafiles for popular models through Mozilla’s docs.

Status
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Active under Mozilla.ai stewardship: created 2023-09-10, 26,190 stars, release 0.10.6 published 2026-09-15, as of 2026-10-07.

The GitHub license field reads NOASSERTION because the repository combines Apache-2.0 with MIT-licensed llama.cpp changes; both Mozilla’s announcement and the README badge state Apache-2.0 as the project license. Development cadence is slower than the local-inference family’s leaders (four releases in 2026), which is the trade of a format-stability project in a fast field.

Strengths
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The lowest-friction distribution story in local inference: one file, no installer, no runtime setup, six OSes.
Cosmopolitan’s reproduction promise, a given llamafile running the same weights the same way indefinitely, is unique in a category where toolchains churn monthly.
GPU and dlopen support inside a single binary, so it is not a toy CPU-only path.
Mozilla stewardship and Apache-2.0 licensing make it the least commercially exposed member of the family.

Cautions
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Massive single binaries (weights plus engine) are clumsy to update: a new model version means a new multi-gigabyte file, where Ollama pulls a manifest.
No model registry or discovery layer; you bring your own GGUF or use Mozilla’s pre-built set.
Slower release cadence than llama.cpp itself, so fresh architecture support lags the upstream engine.
The AVX2 requirement on x64 quick-install binaries excludes older Intel and AMD hardware and some VMs.

Pricing
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Free and open source (Apache-2.0, with llama.cpp changes MIT); there is no paid tier, so pricing does not apply. Costs are your own hardware and the weights you bundle.

Compared to
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Ollama: the registry-and-daemon incumbent; choose llamafile when the artifact must travel as one file, Ollama when a managed local model store matters more.
Magnitude: the self-optimizing inference engine; llamafile optimizes for distribution, Magnitude for kernel performance on your device.
LiteLLM: the self-hosted gateway over cloud APIs; llamafile removes the cloud from the picture entirely.

Bottom line
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Recommended for distributing a fixed model to non-technical recipients, offline environments, and archival use where the weights must stay runnable, and for the six-OS no-install demo. Not as a daily driver for people who churn models weekly, and not for serving agents that want a registry, quotas, or a daemon.

Changes
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2026-10-07 - Created.

References
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https://github.com/mozilla-ai/llamafile - repository, README, licensing badge, whisperfile, and the 0.10 build-system note (fetched 200, 2026-10-07)
https://api.github.com/repos/mozilla-ai/llamafile - stars, created date, push date, and the NOASSERTION license field (fetched 200, 2026-10-07)
https://hacks.mozilla.org/2023/11/introducing-llamafile/ - Mozilla’s launch announcement: the six-OS goal, Justine Tunney’s authorship, and the Apache-2.0 plus MIT split (fetched 200, 2026-10-07)
https://docs.mozilla.ai/llamafile - the Mozilla.ai docs hub, pre-built llamafiles, and whisperfile surface (fetched 200, 2026-10-07)
https://api.github.com/repos/mozilla-ai/llamafile/releases - the 0.10.6 release of 2026-09-15 and the 2026 cadence (fetched 200, 2026-10-07)
https://raw.githubusercontent.com/mozilla-ai/llamafile/HEAD/README.md - the llama.cpp plus Cosmopolitan architecture and the AVX2 x64 compatibility note (fetched 200, 2026-10-07)

single-file on tomrochette.com

llamafile

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single-file on tomrochette.com

llamafile

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