Model reference · open weights
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.
Summary of the tencent/Hunyuan-A13B-Instruct model card, 2026-10-01
What it is
| Released by | tencent |
|---|---|
| Released | 2025-06-25 |
| Parameters | 80.4B |
| VRAM | 161 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … H200 141 GB 11 smaller cards | — | — | — | |
| B200 180 GB | 9 | 2 | all 32K | 176 GB |
| 4× L40S 48 GB tensor parallel | 11 | 2 | all 32K | 44.0 GB a card |
| 2× RTX PRO 6000 Blackwell 96 GB tensor parallel | 16 | 4 | all 32K | 93.8 GB a card |
| 2× H200 141 GB tensor parallel | 98 | 24 | all 32K | 138 GB a card |
| 4× H100 80 GB tensor parallel | 124 | 31 | all 32K | 78.1 GB a card |
| 4× A100 80 GB tensor parallel | 139 | 34 | all 32K | 78.2 GB a card |
| 2× B200 180 GB tensor parallel | 169 | 42 | all 32K | 176 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 163 GB | 166 GB |
| 5 | 167 GB | 183 GB |
| 8 | 170 GB | 196 GB |
| 16 | 179 GB | 230 GB |
| 32 | 196 GB | 299 GB |
| 64 | 230 GB | 436 GB |
One card, with vLLM's small-card settings.
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| Hunyuan-A13B-Instruct (above) ↗ | 80.4B | BF16 | 161 GB | 4× L40S 48 GB |
| Hunyuan-7B-Instruct ↗ | 7.5B | BF16 | 15.0 GB | 2× RTX 3060 12 GB |
| Hunyuan-1.8B-Instruct ↗ | 1.8B | BF16 | 3.6 GB | RTX 3060 12 GB |
From 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.
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.
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.
Note: The following benchmarks are evaluated by TRT-LLM-backend on several base models.
| Model | Hunyuan-Large | Qwen2.5-72B | Qwen3-A22B | Hunyuan-A13B |
|---|---|---|---|---|
| MMLU | 88.40 | 86.10 | 87.81 | 88.17 |
| MMLU-Pro | 60.20 | 58.10 | 68.18 | 67.23 |
| MMLU-Redux | 87.47 | 83.90 | 87.40 | 87.67 |
| BBH | 86.30 | 85.80 | 88.87 | 87.56 |
| SuperGPQA | 38.90 | 36.20 | 44.06 | 41.32 |
| EvalPlus | 75.69 | 65.93 | 77.60 | 78.64 |
| MultiPL-E | 59.13 | 60.50 | 65.94 | 69.33 |
| MBPP | 72.60 | 76.00 | 81.40 | 83.86 |
| CRUX-I | 57.00 | 57.63 | - | 70.13 |
| CRUX-O | 60.63 | 66.20 | 79.00 | 77.00 |
| MATH | 69.80 | 62.12 | 71.84 | 72.35 |
| CMATH | 91.30 | 84.80 | - | 91.17 |
| GSM8k | 92.80 | 91.50 | 94.39 | 91.83 |
| GPQA | 25.18 | 45.90 | 47.47 | 49.12 |
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.
| Topic | Bench | OpenAI-o1-1217 | DeepSeek R1 | Qwen3-A22B | Hunyuan-A13B-Instruct |
|---|---|---|---|---|---|
| Mathematics | AIME 2024AIME 2025MATH | 74.379.296.4 | 79.87094.9 | 85.781.594.0 | 87.376.894.3 |
| Science | GPQA-DiamondOlympiadBench | 7883.1 | 71.582.4 | 71.185.7 | 71.282.7 |
| Coding | LivecodebenchFullstackbenchArtifactsBench | 63.964.638.6 | 65.971.644.6 | 70.765.644.6 | 63.967.843 |
| Reasoning | BBHDROPZebraLogic | 80.490.281 | 83.792.278.7 | 88.990.380.3 | 89.191.184.7 |
| InstructionFollowing | IF-EvalSysBench | 91.882.5 | 88.377.7 | 83.474.2 | 84.776.1 |
| TextCreation | LengthCtrlInsCtrl | 60.174.8 | 55.969 | 53.373.7 | 55.471.9 |
| NLU | ComplexNLUWord-Task | 64.767.1 | 64.576.3 | 59.856.4 | 61.262.9 |
| Agent | BFCL v3 τ-BenchComplexFuncBench C3-Bench | 67.860.447.658.8 | 56.943.841.155.3 | 70.844.640.651.7 | 78.354.761.263.5 |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.