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Hunyuan A13B

Active Parameters

80B

Context Length

256K

Modality

Text

Architecture

Mixture of Experts (MoE)

License

Apache 2.0

Release Date

25 Jun 2025

Knowledge Cutoff

-

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

169.64 GB VRAM

Consumer

9x RTX 4090

24GB VRAM

Datacenter

3x NVIDIA A100

80GB VRAM

Apple Silicon

2x Apple M3 Max

128GB VRAM

256,000 tokens

204.73 GB VRAM

Consumer

10x RTX 4090

24GB VRAM

Datacenter

3x NVIDIA A100

80GB VRAM

Apple Silicon

2x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 4.1k · Context: 256K · Vocab: 128.2kx 32 layersRMSNormPre-AttentionMulti-Head Attention32Q / 8KV headsHead dim: 128+RMSNormPre-FFNSparse MoE FFN (8/65 experts)SwiGLUIntermediate: 3.1k+Final RMSNormOutput Logits

Evaluation Benchmarks

No evaluation benchmarks for Hunyuan A13B available.

Rankings

Overall Rank

-

Coding Rank

-

About Hunyuan A13B

Tencent's Hunyuan A13B is a large language model engineered with a Mixture-of-Experts (MoE) architecture, featuring a total of 80 billion parameters with 13 billion parameters actively engaged during inference. This design approach aims to optimize computational efficiency while maintaining strong performance capabilities. The model is presented as an open-source resource, intended for researchers and developers seeking to deploy advanced AI solutions in contexts where resource allocation requires careful consideration. Its development addresses the challenge of scaling large language models by providing a framework that allows for extensive model capacity without requiring the full activation of all parameters for every task.

The core innovation of Hunyuan A13B lies in its sparse MoE architecture, which dynamically routes input through a subset of specialized "expert" neural networks. Specifically, the architecture comprises 32 layers and incorporates SwiGLU activation functions. It utilizes Grouped Query Attention (GQA) to enhance inference efficiency and reduce memory footprint during processing. A notable feature is its hybrid reasoning mode, enabling the model to adjust its processing depth dynamically between a "fast thinking" mode for rapid responses and a "slow thinking" mode for more intricate, multi-step problem-solving, depending on the complexity of the input. The model was trained on a substantial corpus exceeding 20 trillion tokens, including a significant emphasis on data from scientific, technological, engineering, and mathematical (STEM) domains.

Hunyuan A13B supports an ultra-long context window of up to 256,000 tokens, facilitating comprehensive understanding and generation of content from extensive documents or prolonged conversational sequences. The model has been optimized for agent-based tasks, demonstrating capabilities in areas such as mathematical reasoning, logical analysis, and complex instruction following. Its design emphasizes efficient inference, supporting various quantization formats including FP8 and INT4, which allows for deployment in environments with diverse hardware specifications. This makes it suitable for applications requiring both robust language processing capabilities and optimized computational resource utilization, even potentially on single mid-range GPUs.

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

32

Key-Value Heads

8

Attention Head Dimension

128

Position Embedding

Absolute Position Embedding

RoPE Theta

10,000

Sliding Window Attention

No

Sliding Window Size

-

Sliding Window Ratio

-

Linear Attention

-

Linear Attention Ratio

-

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

4,096

Number of Layers

32

FFN Intermediate Size (Dense)

3,072

Multi-Token Prediction Heads

-

Tokenizer

Vocabulary Size

128,167

Mixture of Experts

Total Expert Parameters

13.0B

Number of Experts

65

Active Experts

8

Shared Experts

1

FFN Intermediate Size (per Expert)

3,072

Dense Layers Before MoE

-

About Hunyuan

Tencent Hunyuan large language models with various capabilities.


Other Hunyuan Models