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Sponsored by
APIMart
AtomicChat / Kimi-K3-GGUF
Model's Last Updated: July 31 2026
image-text-to-text
Introduction of Kimi-K3-GGUF
Model Details of Kimi-K3-GGUF
Kimi K3 , self-quantized to GGUF by Atomic Chat . Built straight from Moonshot AI's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.
Highlights
- 2779.9B parameters : the weights this repo quantizes.
- Context length : 1,048,576 tokens (1M), as published by Moonshot AI.
- 93 layers : Mixture-of-Experts.
- Modalities : the base model handles Text, Image; this repo ships text-only quants, it carries no vision projector.
- Full imatrix ladder : every quant is calibrated with an importance matrix.
- New Architecture : Kimi K3 is built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), and scales up MoE sparsity with a Stable LatentMoE framework that activates 16 out of 896 experts, yielding an approximate 2.5× improvement in overall scaling efficiency over Kimi K2.
- Long-Horizon Coding : Operating with minimal human oversight, Kimi K3 sustains long engineering sessions, navigates massive repositories, and orchestrates terminal tools, from GPU kernel optimization and compiler development to vision-in-the-loop game dev, CAD, and even chip design.
These GGUFs are self-quantized from the original weights , not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.
Always pass
--jinjaso the Kimi K3 chat template is applied. Without it the model can emit malformed turns.
Model Overview
| Property | Value |
|---|---|
| Base model |
moonshotai/Kimi-K3
|
| Parameters | 2779.9B |
| Layers | 93 |
| Experts | 896 routed |
| Context length | 1,048,576 tokens (1M) |
| Vocabulary | 163,840 |
| Modalities | Text, Image in the base model; text only in this repo, it ships no vision projector |
| Architecture |
Mixture-of-Experts, 896 experts, 96 attention heads over 96 KV heads,
KimiK3ForConditionalGeneration
|
| This repo | GGUF quants (imatrix) |
Get started
Run Kimi K3 locally with:
-
Atomic Chat
:
the easiest path. Open the app, search
AtomicChat/Kimi-K3-GGUF, pick a quant, hit Use this model . -
llama.cpp:
llama-server -hf AtomicChat/Kimi-K3-GGUF:None --jinja -c 8192 -
Ollama:
ollama run hf.co/AtomicChat/Kimi-K3-GGUF:None - LM Studio / Jan: search the repo id, download any quant.
Best practices
| Parameter | Value |
|---|---|
| temperature | 1.0. |
Moonshot AI's recommended sampling configuration for
moonshotai/Kimi-K3
.
Run in llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server
./llama.cpp/build/bin/llama-server \
-hf AtomicChat/Kimi-K3-GGUF:None \
--jinja -ngl 99 -c 8192 -fa on
How these were made
-
Download
moonshotai/Kimi-K3(original weights). - Convert to f16 GGUF with llama.cpp .
- Build an importance matrix over our calibration corpus.
-
Quantize the ladder with
--imatrix.
License
Original model by Moonshot AI, released under the other license. Full terms: other . Quantized by Atomic Chat.
Runs of AtomicChat Kimi-K3-GGUF on huggingface.co
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More Information About Kimi-K3-GGUF huggingface.co Model
Url of Kimi-K3-GGUF
Kimi-K3-GGUF huggingface.co Url
Provider of Kimi-K3-GGUF huggingface.co
Other API from AtomicChat
Updated:August 09 2026
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