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Kimi K3

Kimi K3 is a Python implementation of a language-model architecture, with code for small-scale training, inference demonstrations and verification.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Kimi K3 — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Kimi K3 to examine its architecture and training code, or to run small-scale experiments and verification locally. Its large configuration and distributed systems components are useful for study, but the repository does not provide an assembled cluster-scale training workflow.

Who it is for

  • Researchers studying language-model architecture and training methods
  • Developers experimenting with PyTorch model implementations
  • Readers who want to inspect or run a small model and its verification suite

Documented uses

  • Train the nano model on CPU or a supported text corpus.
  • Inspect the parameter count of the 2.8T configuration without allocating it.
  • Run the repository's verification suite.
  • Generate text from the published nano checkpoint or serve it through the documented OpenAI-compatible API.
Before you choose

Requirements & limits

The public GitHub repository specifies an AGPL-3.0 license. The documented runtime requires Python 3.10 or later and PyTorch 2.4 or later.

  • The documented nano training, demos and verification suite can run on CPU.
  • The 2.8T configuration can be inspected for parameter counts without allocating the model.
  • The repository identifies distributed-training components as verified pieces, not an assembled distributed trainer.
  • Triton is optional; the fused KDA path falls back to a reference implementation when Triton is absent.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train the nano model on CPU

Install the runtime and run the documented nano training workflow.

Before you start

  • Python 3.10 or later
  • PyTorch 2.4 or later
  • A CPU is sufficient; a GPU is not required

Steps

  1. Install the repository runtime with the command below.

    Documented command
    pip install -e .

  2. Run the command below.

    Documented command
    bash scripts/train_nano.sh

Expected outcome

The documented end-to-end nano model training run.

Guide limitations

  • This workflow is for the small model; cluster-scale training is out of scope.
  • A GPU may improve training speed, but is not required.
Documentation 1 ↗
Source-based practical guide

Run the verification suite

Install the development dependencies and run the repository's full gate suite.

Before you start

  • Python 3.10 or later
  • PyTorch 2.4 or later

Steps

  1. Install the development dependencies with the command below.

    Documented command
    pip install -e '.[dev]'

  2. Run the command below.

    Documented command
    bash scripts/verify.sh

Expected outcome

The repository's documented verification suite runs.

Guide limitations

  • The suite checks implementation behavior; it does not make the distributed components an assembled cluster-scale trainer.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
TimRots/kimi3 · Official homepage title · retained in its source language
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