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nanguoyu/Kimi-K3-minirun
Kimi-K3-minirun is a machine learning model from nanguoyu. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as other.
The weights of moonshotai/Kimi-K3 at revision 9f62e4e9fffbd0a83ddd60e1c209d828994b3569, repacked byte for byte into the container format Minirun reads. Minirun streams this model from an external SSD on Mac and iPhone…
Downloads · 30 days
0
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Public
Updated Sep 12, 2026
Repo size
1.6 TB
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.mxfp4tile1.4 TB · 93%
From the Hugging Face model README
The weights of moonshotai/Kimi-K3 at revision
9f62e4e9fffbd0a83ddd60e1c209d828994b3569, repacked byte for byte into the container format
Minirun reads. Minirun streams this model from an external SSD on
Mac and iPhone through a memory budget you set, rather than loading it into
memory, so the model does not have to fit in the machine. The reader is open
source: Sources/StorageCore/Container/ in
nanguoyu/minirun-app.
A byte-preserving repack. No requantization, no retraining, no numerical change of any kind:
e8m0 scales, copied verbatim and only reordered;The container files are therefore a rearrangement of the source bytes. Every value in this repository originates from the pinned revision above.
Contents: 93 layer directories, 372 container
files, 1.56 TB total, plus a per-layer manifest
describing offsets and shapes. Vision tensors (vision_tower.*,
mm_projector.*) are not included; this is the text stack only.
Get the app. On a Mac, download Minirun.dmg. On an iPhone,
install TestFlight and open
testflight.apple.com/join/QuYTybjV
on the phone; the source is at nanguoyu/minirun-app if you
would rather build it.
Point it at this repository. In Settings → Storage, use Add a folder… to register a folder on an external NVMe drive. In Settings → Models, open Find Models, select this repository and press Download -- or point Minirun at a copy you already have. Run Verify all files, then start a chat.
What to expect.
| On disk | 1.56 TB |
| MacBook Pro (M1 Pro, 32 GB, USB4 enclosure) | ≈ 70 s / token at an 8 GB budget |
| iPhone 16 Pro (USB 3 port, powered dock) | ≈ 220 s / token at a 5.8 GB budget, replies capped at 2 tokens per turn |
The iPhone figure is a demonstration that the size ceiling is gone, not a way to chat. Speeds change with the drive, the cable and the budget; the enclosure and cable matter more than the SSD — USB4 against a 10 Gb/s hub is about 3× on the same bytes — and the iPhone needs a powered dock.
Requirements. An Apple-silicon Mac on macOS 15 or later; an iPhone 15 Pro or later on iOS 18 or later; an external NVMe drive with room for the model.
minirun.dev · Docs · This model · GitHub
| Source model | moonshotai/Kimi-K3 |
| Source revision | 9f62e4e9fffbd0a83ddd60e1c209d828994b3569 |
| Relationship | byte-preserving repack (no requantization) |
This repository redistributes model weights owned by Moonshot AI under the
Kimi K3 License, reproduced verbatim in LICENSE and copied
unmodified from the source repository at the pinned revision above.
Copyright (c) 2026 Moonshot AI.
Two clauses of that licence apply to anyone using these files downstream, and are highlighted here rather than left to be discovered:
§4 exempts purely internal use. The weights are provided "as is", without
warranty of any kind, per §5. Refer to LICENSE for the governing
text; the summary above is not a substitute for it.