I implemented the Kimi K3 paper from scratch in PyTorch
Independent from-scratch implementation of the Kimi K3 architecture (arXiv:2607.24653v1): KDA, NoPE, latent-space MoE, and the systems co-designs. Table 1 reproduced to 0.09%. - TimRots/kimi3
I implemented the Kimi K3 paper from scratch in PyTorch launched on August 2, 2026 with 10 votes, #1,032 of 23,274 launches that month and more than 90% of that year's launches.
In plain words
This is a PyTorch implementation of the Kimi K3 paper, built from scratch by the creator. It's designed for researchers and developers interested in studying or experimenting with the K3 model architecture. The project is available on GitHub for those wanting to explore the implementation details or use it as a foundation for their own work.
written from the facts on this page · September 2026
From the sources
An implementation of the architecture, pre-training protocol, systems co-designs, and post-training framework described in Kimi K3: Open Frontier Intelligence (arXiv:2607.24653v1). Scale is configuration. Nothing in kimi3/ branches on model size, so the same code path runs a 20M-parameter model on a laptop CPU and describes the paper's 2.8T configuration. This repository executes the small end and the verification suite. Training at cluster scale is out of scope, and docs/PAPER_MAP.md marks which components are wired into a runnable path and which are libraries. Python ≥ 3.10, PyTorch ≥ 2.4. No GPU is required. The test suite, the parameter audit of the 2.8T config, the demos, and the nano…from github.com
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