We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B overall criteria with 37B activated for each token. To accomplish effective inference and economical training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free technique for load balancing and sets a multi-token prediction training objective for more powerful efficiency. We pre-train DeepSeek-V3 on 14.8 trillion varied and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to completely harness its capabilities. Comprehensive assessments reveal that DeepSeek-V3 surpasses other open-source models and attains efficiency similar to leading closed-source models. Despite its exceptional efficiency, DeepSeek-V3 needs just 2.788 M H800 GPU hours for its complete training. In addition, its training process is extremely steady. Throughout the whole training procedure, we did not experience any irrecoverable loss spikes or perform any rollbacks.


2. Model Summary


Architecture: Innovative Load Balancing Strategy and Training Objective


- On top of the effective architecture of DeepSeek-V2, we leader an auxiliary-loss-free technique for load balancing, which lessens the performance destruction that develops from motivating load balancing.
- We examine a Multi-Token Prediction (MTP) goal and show it advantageous to model performance. It can also be utilized for speculative decoding for reasoning velocity.


Pre-Training: Towards Ultimate Training Efficiency


- We develop an FP8 blended accuracy training framework and, for the first time, confirm the expediency and efficiency of FP8 training on a very massive model.
- Through co-design of algorithms, structures, and hardware, we overcome the interaction traffic jam in cross-node MoE training, almost attaining complete computation-communication overlap.
This significantly boosts our training performance and lowers the training costs, allowing us to even more scale up the design size without additional overhead.
- At an affordable cost of only 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the presently strongest open-source base model. The subsequent training stages after pre-training need only 0.1 M GPU hours.


Post-Training: Knowledge Distillation from DeepSeek-R1


- We present an ingenious method to boil down thinking abilities from the long-Chain-of-Thought (CoT) design, particularly from among the DeepSeek R1 series designs, into basic LLMs, particularly DeepSeek-V3. Our pipeline elegantly includes the confirmation and reflection patterns of R1 into DeepSeek-V3 and significantly improves its reasoning efficiency. Meanwhile, we also keep a control over the output style and length of DeepSeek-V3.


3. Model Downloads


The overall size of DeepSeek-V3 designs on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **


To make sure ideal performance and versatility, we have partnered with open-source communities and hardware vendors to supply numerous ways to run the design in your area. For detailed guidance, take a look at Section 6: How_to Run_Locally.


For designers looking to dive much deeper, we advise exploring README_WEIGHTS. md for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active advancement within the neighborhood, and we welcome your contributions and feedback.


4. Evaluation Results


Base Model


Standard Benchmarks


Best outcomes are displayed in vibrant. Scores with a gap not going beyond 0.3 are thought about to be at the exact same level. DeepSeek-V3 attains the very best performance on the majority of criteria, particularly on mathematics and code tasks. For more examination information, please examine our paper.


Context Window


Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 carries out well throughout all context window lengths approximately 128K.


Chat Model


Standard Benchmarks (Models larger than 67B)


All models are examined in a setup that limits the output length to 8K. Benchmarks including less than 1000 samples are tested multiple times utilizing differing temperature level settings to derive robust outcomes. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive efficiency against frontier closed-source models.


Open Ended Generation Evaluation


English open-ended conversation assessments. For AlpacaEval 2.0, we utilize the length-controlled win rate as the metric.


5. Chat Website & API Platform


You can chat with DeepSeek-V3 on DeepSeek's main website: chat.deepseek.com


We likewise offer OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com


6. How to Run Locally


DeepSeek-V3 can be deployed locally using the following hardware and open-source neighborhood software application:


DeepSeek-Infer Demo: We provide an easy and light-weight demonstration for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables efficient FP8 and BF16 reasoning for regional and cloud release.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 assistance coming soon.
vLLM: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 design on AMD GPUs by means of SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend gadgets.
Since FP8 training is natively embraced in our structure, we only offer FP8 weights. If you require BF16 weights for experimentation, you can utilize the provided conversion script to perform the change.


Here is an example of converting FP8 weights to BF16:


Hugging Face's Transformers has not been directly supported yet. **


6.1 Inference with DeepSeek-Infer Demo (example just)


System Requirements


Note


Linux with Python 3.10 just. Mac and Windows are not supported.


Dependencies:


Model Weights & Demo Code Preparation


First, clone our DeepSeek-V3 GitHub repository:


Navigate to the inference folder and set up dependences listed in requirements.txt. Easiest way is to use a plan supervisor like conda or uv to develop a new virtual environment and set up the dependences.


Download the design weights from Hugging Face, and put them into/ path/to/DeepSeek-V 3 folder.


Model Weights Conversion


Convert Hugging Face model weights to a specific format:


Run


Then you can chat with DeepSeek-V3:


Or batch reasoning on an offered file:


6.2 Inference with SGLang (suggested)


SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering modern latency and throughput efficiency among open-source structures.


Notably, SGLang v0.4.1 fully supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly flexible and robust service.


SGLang likewise supports multi-node tensor parallelism, allowing you to run this design on multiple network-connected devices.


Multi-Token Prediction (MTP) is in advancement, and progress can be tracked in the optimization strategy.


Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/t … eepseek_v3


6.3 Inference with LMDeploy (advised)


LMDeploy, a flexible and high-performance inference and serving structure customized for large language designs, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, flawlessly integrating with PyTorch-based workflows.


For detailed detailed guidelines on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960


6.4 Inference with TRT-LLM (suggested)


TensorRT-LLM now supports the DeepSeek-V3 model, offering precision alternatives such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in development and will be released soon. You can access the customized branch of TRTLLM particularly for DeepSeek-V3 assistance through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/ … epseek_v3.


6.5 Inference with vLLM (advised)


vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic strategies, vLLM uses pipeline parallelism allowing you to run this model on multiple machines connected by networks. For in-depth assistance, please describe the vLLM directions. Please do not hesitate to follow the improvement plan also.


6.6 Recommended Inference Functionality with AMD GPUs


In collaboration with the AMD group, we have actually achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For in-depth assistance, please describe the SGLang guidelines.


6.7 Recommended Inference Functionality with Huawei Ascend NPUs


The MindIE structure from the Huawei Ascend community has actually effectively adjusted the BF16 variation of DeepSeek-V3. For detailed assistance on Ascend NPUs, please follow the directions here.


7. License


This code repository is licensed under the MIT License. Using DeepSeek-V3 Base/Chat models goes through the Model License. DeepSeek-V3 series (consisting of Base and Chat) supports business usage.