Kimi-K2-Instruct

Moonshot AI's Mixture-of-Experts model with 1 trillion parameters

Focus on agentic intelligence and tool usage

The Kimi-K2-Instruct model at a glance

With Kimi-K2, Moonshot AI presents a new state-of-the-art language model specifically designed for agentic applications and demanding tool usage. The combination of huge MoE design, innovative MuonClip optimizer and large-scale RL training makes Kimi-K2-Instruct one of the most powerful open-source models for complex tasks.

Name:

Kimi-K2-Instruct

Developer:

Moonshot AI

Publication:

July 2025

License:

Modified MIT license (commercial use permitted)

Model type:

Mixture-of-Experts (MoE) language model

Parameters:

1 trillion (of which 32B active per token)

Architecture:

61 layers, 384 experts, 8 experts per token, MLA Attention, SwiGLU activation

Tokenizer:

160k vocabulary

Context length:

128,000 tokens

Variants of the Kimi-K2 series

  • Kimi-K2-Base: The pure base model for your own fine-tuning and research.
  • Kimi-K2-Instruct: The post-trained model for general chat and agentic applications (recommended for most users).

Specialties from Kimi-K2-Instruct

Agentic Intelligence & Tool Usage

Kimi-K2-Instruct has been specifically developed for use as an agent: It can control external tools, solve complex tasks autonomously and is optimized for multi-turn interactions.

MuonClip Optimizer

The training was carried out with the innovative MuonClip Optimizer, which was specially developed for stability and efficiency when training huge MoE models.

Large context window

With a context length of 128,000 tokens, Kimi-K2-Instruct is also suitable for very long documents, complex dialogs and extensive data analyses.
Individual AI consulting

Is Kimi-K2-Instruct the right model for you?

We would be happy to advise you individually on which AI model suits your requirements. Arrange a no-obligation initial consultation with our AI experts and exploit the full potential of AI for your project!

The post-training pipeline for Kimi-K2-Instruct

Training data & training process

Kimi-K2-Instruct was trained on an exceptionally broad database, including 15.5 trillion tokens as well as specially synthesized data for tool usage and agentic tasks. The pre-training was complemented by a comprehensive reinforcement learning process covering both verifiable and non-verifiable tasks.

With the specially developed MuonClip Optimizer, the scaling of this large-scale MoE model could be carried out stably and efficiently – a crucial basis for reliable use in real, complex scenarios.

Hardware-Anforderungen (Inferenz)

  • Recommended inference engines: vLLM, SGLang, KTransformers, TensorRT-LLM
  • Checkpoints available in block-fp8 format
  • Several high-end GPUs are recommended for real-time inference (see Deployment Guide for details)
  • Quantized variants and adapters available
Powerful & performant

Recommended use cases for Kimi-K2-Instruct

Is Kimi-K2-Instruct the right AI model for your individual application? We will be happy to advise you comprehensively and personally.

AI assistants and chatbots with tool integration
Automated problem solving and decision support
Complex code generation and software development
Scientific research, mathematics, data analysis
Agentic workflows and autonomous systems
Kimi-K2-Instruct
Kimi-K2-Instruct: For advanced agentic workflows

Ready for autonomous systems with real tool integration?

Whether as an intelligent AI assistant, for automated code generation or for integration into scientific systems: Kimi-K2-Instruct provides the necessary architecture, performance and openness for productive use. Our team of experts will support you with selection, fine-tuning and hosting – fully managed in our German GPU Cloud if required.