Kimi-VL-A3B-Thinking
Active Parameters
16B
Context Length
128K
Modality
Multimodal
Architecture
Mixture of Experts (MoE)
License
MIT License
Release Date
10 Apr 2025
Knowledge Cutoff
Oct 2024
Technical Specifications
Total Expert Parameters
3.0B
Number of Experts
64
Active Experts
2
Attention Structure
Multi-Head Attention
Hidden Dimension Size
2048
Number of Layers
27
Attention Heads
16
Key-Value Heads
16
Activation Function
SwigLU
Normalization
RMS Normalization
Position Embedding
Absolute Position Embedding
Kimi-VL-A3B-Thinking
Kimi-VL-A3B-Thinking is an advanced vision-language model (VLM) developed by Moonshot AI, engineered to bridge the gap between efficient parameter utilization and high-fidelity multimodal reasoning. Architecturally, it is built upon the Mixture-of-Experts (MoE) framework of the Moonlight LLM series, integrating a proprietary native-resolution visual encoder known as MoonViT via an MLP projector. The model is specifically optimized for long-horizon cognitive tasks through supervised fine-tuning and reinforcement learning, allowing it to generate extended chains of thought (CoT) when processing complex visual and textual inputs.
The system utilizes a sparse MoE design comprising 16 billion total parameters, with only approximately 2.8 billion parameters activated during any single inference step. The language decoder follows a configuration similar to the DeepSeek-V3 architecture, featuring Multi-head Latent Attention (MLA) and a specialized gating mechanism that routes tokens through 64 routed experts. This structural innovation enables the model to handle diverse input resolutions and aspect ratios without downsampling, preserving the fidelity of visual data for tasks such as optical character recognition (OCR) and college-level academic analysis.
Functionally, Kimi-VL-A3B-Thinking supports an expansive context window of 128,000 tokens, facilitating the ingestion of lengthy documents, multi-image sequences, and video content. The "Thinking" variant is tailored for scenarios requiring multi-step mathematical problem-solving, document comprehension, and autonomous agent interactions. By leveraging Flash-Attention 2 and supporting native half-precision formats, the model maintains high throughput and computational efficiency across a broad spectrum of multimodal reasoning applications.
About Kimi-VL
Kimi-VL by Moonshot AI is an efficient, open-source Mixture-of-Experts vision-language model. It employs a native-resolution MoonViT encoder and an MoE language model, activating 2.8 billion parameters. The model handles high-resolution visual inputs and processes contexts up to 128K tokens. A "Thinking" variant provides enhanced long-horizon reasoning.
Other Kimi-VL Models
Evaluation Benchmarks
No evaluation benchmarks for Kimi-VL-A3B-Thinking available.
Rankings
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Coding Rank
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GPU Requirements
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Context Size: 1,024 tokens