Model reference · open weights
Hunyuan is an open-weight language model from tencent. Hunyuan-1.8B-Instruct (BF16) weighs 3.6 GB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the tencent/Hunyuan-A13B-Instruct model card, 2026-10-01. The estimate below is for another build of the family.
What it is
| Released by | tencent |
|---|---|
| Released | 2025-06-25 |
| Parameters | 80.4B |
| VRAM | 3.6 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB | 13 | 3 | 110K | 11.6 GB |
| RTX 4060 Ti 16 GB | 20 | 5 | 166K | 15.4 GB |
| RTX 3090 24 GB | 35 | 8 | all 256K | 23.4 GB |
| RTX 4090 24 GB | 35 | 8 | all 256K | 23.4 GB |
| RTX 5090 32 GB | 49 | 12 | all 256K | 31.0 GB |
| L40S 48 GB | 74 | 18 | all 256K | 44.0 GB |
| A100 80 GB | 137 | 34 | all 256K | 78.2 GB |
| H100 80 GB | 130 | 32 | all 256K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 159 | 39 | all 256K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 185 | 46 | all 256K | 107 GB |
| H200 141 GB | 242 | 60 | all 256K | 138 GB |
| B200 180 GB | 312 | 78 | all 256K | 176 GB |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 4.8 GB | 6.4 GB |
| 5 | 6.9 GB | 15.0 GB |
| 8 | 8.5 GB | 21.4 GB |
| 16 | 12.8 GB | 38.6 GB |
| 32 | 21.4 GB | 72.9 GB |
| 64 | 38.6 GB | 142 GB |
One card, with vLLM's small-card settings.
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| Hunyuan-A13B-Instruct ↗ | 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 (above) ↗ | 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.