技术规格

系统要求

不同量化方法和上下文大小的显存要求

Kimi K2-Instruct

Kimi K2-Instruct is an advanced Mixture-of-Experts (MoE) language model developed by Moonshot AI. This model incorporates 1 trillion total parameters, with approximately 32 billion parameters activated during each inference pass. Its core purpose is to deliver state-of-the-art agentic intelligence, facilitating sophisticated tool utilization, advanced code generation, and autonomous problem-solving across various domains. As a post-trained instruction-following variant, Kimi K2-Instruct is optimized for general-purpose conversational tasks and complex agentic workflows, operating as a reflex-grade model designed for direct application.

The architectural design of Kimi K2-Instruct features a Mixture-of-Experts paradigm, leveraging 384 specialized experts, with 8 active experts dynamically selected per token during inference. The model comprises 61 layers and employs a Multi-head Local Attention (MLA) mechanism with 64 attention heads. A key innovation in its training methodology is the MuonClip optimizer, developed by Moonshot AI, which ensures training stability at the expansive scale of 15.5 trillion tokens. The architecture prioritizes long-context efficiency, supporting a substantial context window of 128,000 tokens. The activation function employed within the model is SwiGLU, complemented by Rotary Position Embeddings (RoPE).

Kimi K2-Instruct is engineered for demanding applications, including complex, multi-step reasoning tasks and analytical workflows that necessitate profound comprehension. Its capabilities encompass advanced code generation, ranging from foundational scripting to intricate software development and debugging, along with robust support for multilingual applications. The model exhibits strong tool-calling capabilities, enabling it to autonomously interpret user intentions and orchestrate external tools and APIs to accomplish intricate objectives. Practical use cases include automating development workflows, generating comprehensive data analysis reports, and facilitating interactive task planning by seamlessly integrating multiple external services.