Kimi-K2

Kimi K2 represents a true breakthrough in the scaling of very large language models. The model is built on the Mixture-of-Experts (MoE) architecture and includes 384 experts, of which only 8 are activated for processing each token, along with one shared expert, delivering incredible efficiency while maintaining the power of a full-scale model. This architecture enables Kimi K2 to adapt computational resources to specific tasks—using minimal computation for simple queries and activating specialized experts for more complex tasks.

The model features 61 layers with 64 attention heads and supports a context window of 128,000 tokens, allowing it to process entire codebases or large documents in a single session. The use of the Multi-Layer Attention (MLA) mechanism and the SwiGLU activation function ensures optimal performance when working with long sequences.One of Kimi K2’s landmark technical achievements is the MuonClip optimizer, which solves the critical problem of training instability in trillion-parameter-scale models. This innovation enabled Moonshot AI to train the massive model on 15.5 trillion data tokens with zero training instability—an unprecedented achievement in AI.

According to its developers, Kimi K2 is the world’s first model specifically optimized for agent-based scenarios. Unlike traditional chatbot-style models, Kimi K2 is trained to autonomously use tools, execute code, analyze data, and orchestrate complex workflows. The model has undergone post-training on millions of synthetic dialogues simulating real-world tool usage scenarios, giving it a practical advantage in tool selection and multi-step task execution. Native support for the Model Context Protocol (MCP) allows Kimi K2 to seamlessly integrate with external systems, APIs, and development tools.The model is capable of automatically decomposing complex tasks, selecting appropriate tools, and executing multi-stage workflows with minimal human intervention—ranging from building interactive web applications to automating software development processes.

Announce Date: 11.07.2025
Parameters: 1000B
Experts: 384
Activated: 32B
Context: 131K
Attention Type: Multi-head Latent Attention
VRAM requirements: 474.3 GB using 4 bits quantization
Developer: Moonshot AI
Transformers Version: 4.48.3
License: MIT

Use our pre-built public endpoints to test inference and explore Kimi-K2 capabilities.

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

• maximize endpoint performance,
• enable full context for long sequences,
• ensure top-tier security for data processing in an isolated, dedicated environment,
• use custom weights, such as fine-tuned models or LoRA adapters.

Contact our dedicated neural networks support team at nn@immers.cloud or send your request to the sales department at sale@immers.cloud.