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deepseek-ai / DeepSeek-V3.2-Exp
Model's Last Updated: November 18 2025
text-generation
Introduction of DeepSeek-V3.2-Exp
Model Details of DeepSeek-V3.2-Exp
DeepSeek-V3.2-Exp
Introduction
We are excited to announce the official release of DeepSeek-V3.2-Exp, an experimental version of our model. As an intermediate step toward our next-generation architecture, V3.2-Exp builds upon V3.1-Terminus by introducing DeepSeek Sparse Attention—a sparse attention mechanism designed to explore and validate optimizations for training and inference efficiency in long-context scenarios.
This experimental release represents our ongoing research into more efficient transformer architectures, particularly focusing on improving computational efficiency when processing extended text sequences.
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DeepSeek Sparse Attention (DSA) achieves fine-grained sparse attention for the first time, delivering substantial improvements in long-context training and inference efficiency while maintaining virtually identical model output quality.
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To rigorously evaluate the impact of introducing sparse attention, we deliberately aligned the training configurations of DeepSeek-V3.2-Exp with V3.1-Terminus. Across public benchmarks in various domains, DeepSeek-V3.2-Exp demonstrates performance on par with V3.1-Terminus.
| Benchmark | DeepSeek-V3.1-Terminus | DeepSeek-V3.2-Exp |
|---|---|---|
| Reasoning Mode w/o Tool Use | ||
| MMLU-Pro | 85.0 | 85.0 |
| GPQA-Diamond | 80.7 | 79.9 |
| Humanity's Last Exam | 21.7 | 19.8 |
| LiveCodeBench | 74.9 | 74.1 |
| AIME 2025 | 88.4 | 89.3 |
| HMMT 2025 | 86.1 | 83.6 |
| Codeforces | 2046 | 2121 |
| Aider-Polyglot | 76.1 | 74.5 |
| Agentic Tool Use | ||
| BrowseComp | 38.5 | 40.1 |
| BrowseComp-zh | 45.0 | 47.9 |
| SimpleQA | 96.8 | 97.1 |
| SWE Verified | 68.4 | 67.8 |
| SWE-bench Multilingual | 57.8 | 57.9 |
| Terminal-bench | 36.7 | 37.7 |
How to Run Locally
HuggingFace
We provide an updated inference demo code in the inference folder to help the community quickly get started with our model and understand its architectural details.
First convert huggingface model weights to the the format required by our inference demo. Set
MP
to match your available GPU count:
cd inference
export EXPERTS=256
python convert.py --hf-ckpt-path ${HF_CKPT_PATH} --save-path ${SAVE_PATH} --n-experts ${EXPERTS} --model-parallel ${MP}
Launch the interactive chat interface and start exploring DeepSeek's capabilities:
export CONFIG=config_671B_v3.2.json
torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactive
SGLang
Installation with Docker
# H200
docker pull lmsysorg/sglang:dsv32
# MI350
docker pull lmsysorg/sglang:dsv32-rocm
# NPUs
docker pull lmsysorg/sglang:dsv32-a2
docker pull lmsysorg/sglang:dsv32-a3
Launch Command
python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --dp 8 --page-size 64
vLLM
vLLM provides day-0 support of DeepSeek-V3.2-Exp. See the recipes for up-to-date details.
Open-Source Kernels
For TileLang kernels with better readability and research-purpose design , please refer to TileLang .
For high-performance CUDA kernels , indexer logit kernels (including paged versions) are available in DeepGEMM . Sparse attention kernels are released in FlashMLA .
License
This repository and the model weights are licensed under the MIT License .
Citation
@misc{deepseekai2024deepseekv32,
title={DeepSeek-V3.2-Exp: Boosting Long-Context Efficiency with DeepSeek Sparse Attention},
author={DeepSeek-AI},
year={2025},
}
Contact
If you have any questions, please raise an issue or contact us at [email protected] .
Runs of deepseek-ai DeepSeek-V3.2-Exp on huggingface.co
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More Information About DeepSeek-V3.2-Exp huggingface.co Model
Url of DeepSeek-V3.2-Exp
DeepSeek-V3.2-Exp huggingface.co Url
Provider of DeepSeek-V3.2-Exp huggingface.co
Other API from deepseek-ai
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