Model reference · open weights

Hunyuan

LLMs tencent Text gen · MoE 3 builds Its own licence terms 48k dl/mo

Hunyuan is an open-weight language model from tencent. Hunyuan-A13B-Instruct (BF16) weighs 161 GB; the smallest configuration that runs it is 4× L40S 48 GB.

  • Hunyuan-A13B-Instruct is a text-generation model developed by Tencent that uses a Mixture-of-Experts architecture with 80.4 billion total parameters and 13 billion active parameters.
  • It supports a native context window of 256K tokens and features hybrid reasoning modes for both fast and slow thinking.
  • The model is released under an "other" license and is optimized for agent tasks and general-purpose applications.

Summary of the tencent/Hunyuan-A13B-Instruct model card, 2026-10-01

What it is

Released bytencent
Released2025-06-25
Parameters80.4B
VRAM161 GB for the weights

What it runs on

Memory and cards for Hunyuan-A13B-Instruct (BF16)

161 GBweights, file size
131 MBcache per 1K tokens
653 MBruntime overhead, at least
32,768 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … H200 141 GB
11 smaller cards
———
B200 180 GB92all 32K176 GB
4× L40S 48 GB
tensor parallel
112all 32K44.0 GB a card
2× RTX PRO 6000 Blackwell 96 GB
tensor parallel
164all 32K93.8 GB a card
2× H200 141 GB
tensor parallel
9824all 32K138 GB a card
4× H100 80 GB
tensor parallel
12431all 32K78.1 GB a card
4× A100 80 GB
tensor parallel
13934all 32K78.2 GB a card
2× B200 180 GB
tensor parallel
16942all 32K176 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
1163 GB166 GB
5167 GB183 GB
8170 GB196 GB
16179 GB230 GB
32196 GB299 GB
64230 GB436 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
Hunyuan-A13B-Instruct (above) ↗ 80.4BBF16 161 GB4× L40S 48 GB
Hunyuan-7B-Instruct ↗ 7.5BBF16 15.0 GB2× RTX 3060 12 GB
Hunyuan-1.8B-Instruct ↗ 1.8BBF16 3.6 GBRTX 3060 12 GB

From the model card

What tencent says about Hunyuan

Read the model card

🤗 Hugging Face  |   🖥️ Official Website  |   🕖 HunyuanAPI  |   🕹️ Demo  |   🤖 ModelScope

Welcome to the official repository of Hunyuan-A13B, an innovative and open-source large language model (LLM) built on a fine-grained Mixture-of-Experts (MoE) architecture. Designed for efficiency and scalability, Hunyuan-A13B delivers cutting-edge performance with minimal computational overhead, making it an ideal choice for advanced reasoning and general-purpose applications, especially in resource-constrained environments.

Model Introduction

With the rapid advancement of artificial intelligence technology, large language models (LLMs) have achieved remarkable progress in natural language processing, computer vision, and scientific tasks. However, as model scales continue to expand, optimizing resource consumption while maintaining high performance has become a critical challenge. To address this, we have explored Mixture of Experts (MoE) architectures. The newly introduced Hunyuan-A13B model features a total of 80 billion parameters with 13 billion active parameters. It not only delivers high-performance results but also achieves optimal resource efficiency, successfully balancing computational power and resource utilization.

Key Features and Advantages

  • Compact yet Powerful: With only 13 billion active parameters (out of a total of 80 billion), the model delivers competitive performance on a wide range of benchmark tasks, rivaling much larger models.
  • Hybrid Reasoning Support: Supports both fast and slow thinking modes, allowing users to flexibly choose according to their needs.
  • Ultra-Long Context Understanding: Natively supports a 256K context window, maintaining stable performance on long-text tasks.
  • Enhanced Agent Capabilities: Optimized for agent tasks, achieving leading results on benchmarks such as BFCL-v3, τ-Bench and C3-Bench.
  • Efficient Inference: Utilizes Grouped Query Attention (GQA) and supports multiple quantization formats, enabling highly efficient inference.

Why Choose Hunyuan-A13B?

As a powerful yet computationally efficient large model, Hunyuan-A13B is an ideal choice for researchers and developers seeking high performance under resource constraints. Whether for academic research, cost-effective AI solution development, or innovative application exploration, this model provides a robust foundation for advancement.

 

Related News

  • 2025.6.27 We have open-sourced Hunyuan-A13B-Pretrain , Hunyuan-A13B-Instruct , Hunyuan-A13B-Instruct-FP8 , Hunyuan-A13B-Instruct-GPTQ-Int4 on Hugging Face. In addition, we have released a technical report and a training and inference operation manual, which provide detailed information about the model’s capabilities as well as the operations for training and inference.

Benchmark

Note: The following benchmarks are evaluated by TRT-LLM-backend on several base models.

ModelHunyuan-LargeQwen2.5-72BQwen3-A22BHunyuan-A13B
MMLU88.4086.1087.8188.17
MMLU-Pro60.2058.1068.1867.23
MMLU-Redux87.4783.9087.4087.67
BBH86.3085.8088.8787.56
SuperGPQA38.9036.2044.0641.32
EvalPlus75.6965.9377.6078.64
MultiPL-E59.1360.5065.9469.33
MBPP72.6076.0081.4083.86
CRUX-I57.0057.63-70.13
CRUX-O60.6366.2079.0077.00
MATH69.8062.1271.8472.35
CMATH91.3084.80-91.17
GSM8k92.8091.5094.3991.83
GPQA25.1845.9047.4749.12

Hunyuan-A13B-Instruct has achieved highly competitive performance across multiple benchmarks, particularly in mathematics, science, agent domains, and more. We compared it with several powerful models, and the results are shown below.

TopicBenchOpenAI-o1-1217DeepSeek R1Qwen3-A22BHunyuan-A13B-Instruct
MathematicsAIME 2024AIME 2025MATH74.379.296.479.87094.985.781.594.087.376.894.3
ScienceGPQA-DiamondOlympiadBench7883.171.582.471.185.771.282.7
CodingLivecodebenchFullstackbenchArtifactsBench63.964.638.665.971.644.670.765.644.663.967.843
ReasoningBBHDROPZebraLogic80.490.28183.792.278.788.990.380.389.191.184.7
InstructionFollowingIF-EvalSysBench91.882.588.377.783.474.284.776.1
TextCreationLengthCtrlInsCtrl60.174.855.96953.373.755.471.9
NLUComplexNLUWord-Task64.767.164.576.359.856.461.262.9
AgentBFCL v3 τ-BenchComplexFuncBench C3-Bench67.860.447.658.856.943.841.155.370.844.640.651.778.354.761.263.5

 

Use with tra

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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