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Fecha de fundación julio 5, 1947
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Sobre la Entidad
We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B overall specifications with 37B activated for each token. To achieve efficient inference and affordable training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely verified in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free technique for load balancing and sets a multi-token prediction training goal for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to completely harness its abilities. Comprehensive assessments expose that DeepSeek-V3 surpasses other open-source designs and attains efficiency comparable to leading closed-source models. Despite its outstanding performance, DeepSeek-V3 needs just 2.788 M H800 GPU hours for its full training. In addition, its training procedure is incredibly steady. Throughout the entire 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 reduces the performance destruction that emerges from encouraging load balancing. – We investigate a Multi-Token Prediction (MTP) objective and prove it useful to design performance. It can also be used for speculative decoding for inference velocity.
Pre-Training: Towards Ultimate Training Efficiency
– We develop an FP8 combined accuracy training framework and, for the very first time, validate the expediency and effectiveness of FP8 training on an model. – Through co-design of algorithms, frameworks, and hardware, we overcome the communication traffic jam in cross-node MoE training, almost accomplishing complete computation-communication overlap. This substantially enhances our training effectiveness and reduces the training expenses, enabling us to further scale up the design size without extra overhead. – At an economical cost of only 2.664 M H800 GPU hours, we finish the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the currently strongest open-source base design. The subsequent training phases after pre-training need only 0.1 M GPU hours.
Post-Training: Knowledge Distillation from DeepSeek-R1
– We present an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) design, particularly from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly includes the verification and reflection patterns of R1 into DeepSeek-V3 and significantly improves its reasoning performance. Meanwhile, we likewise preserve a control over the output style and length of DeepSeek-V3.
3. Model Downloads
The overall size of DeepSeek-V3 models on Hugging Face is 685B, that includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **
To guarantee optimum efficiency and versatility, we have actually partnered with open-source communities and hardware suppliers to provide several ways to run the model locally. For step-by-step guidance, take a look at Section 6: How_to Run_Locally.
For designers looking to dive much deeper, we recommend checking out README_WEIGHTS. md for information on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is presently under active development within the community, and we invite your contributions and feedback.
4. Evaluation Results
Base Model
Standard Benchmarks
Best outcomes are shown in strong. Scores with a gap not surpassing 0.3 are thought about to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, specifically on math and code jobs. For more evaluation information, please inspect our paper.
Context Window

Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 carries out well throughout all context window lengths as much as 128K.
Chat Model
Standard Benchmarks (Models bigger than 67B)
All designs are evaluated in a configuration that restricts the output length to 8K. Benchmarks containing less than 1000 samples are checked several times using differing temperature level settings to obtain robust last outcomes. DeepSeek-V3 stands as the best-performing open-source design, and likewise displays competitive performance versus frontier closed-source designs.
Open Ended Generation Evaluation
English open-ended conversation assessments. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.
5. Chat Website & API Platform
You can talk with DeepSeek-V3 on DeepSeek’s main website: chat.deepseek.com
We also offer OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com
6. How to Run Locally
DeepSeek-V3 can be released locally using the following hardware and open-source community software:

DeepSeek-Infer Demo: We provide a simple and lightweight demonstration for FP8 and BF16 inference. SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly. LMDeploy: Enables efficient FP8 and BF16 inference for local and cloud deployment. TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming quickly. vLLM: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism. AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs through SGLang in both BF16 and FP8 modes. Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend gadgets. Since FP8 training is natively adopted in our framework, we just supply FP8 weights. If you require BF16 weights for experimentation, you can utilize the offered conversion script to perform the transformation.
Here is an example of transforming FP8 weights to BF16:
Hugging Face’s Transformers has actually not been directly supported yet. **
6.1 Inference with DeepSeek-Infer Demo (example only)
System Requirements
Note
Linux with Python 3.10 only. Mac and Windows are not supported.
Dependencies:
Model Weights & Demo Code Preparation
First, clone our DeepSeek-V3 GitHub repository:
Navigate to the reasoning folder and set up dependencies listed in requirements.txt. Easiest method is to utilize a bundle supervisor like conda or uv to produce a brand-new virtual environment and install the reliances.
Download the model weights from Hugging Face, and put them into/ path/to/DeepSeek-V 3 folder.
Model Weights Conversion
Convert Hugging Face model weights to a particular format:
Run
Then you can chat with DeepSeek-V3:
Or batch reasoning on a provided file:
6.2 Inference with SGLang (advised)
SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering advanced latency and throughput efficiency among open-source structures.
Notably, SGLang v0.4.1 completely supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly flexible and robust solution.

SGLang also supports multi-node tensor parallelism, allowing you to run this design on several network-connected makers.
Multi-Token Prediction (MTP) is in advancement, and development can be tracked in the optimization strategy.
Here are the launch instructions from the SGLang group: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3
6.3 Inference with LMDeploy (suggested)
LMDeploy, a flexible and high-performance inference and serving framework tailored for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online release capabilities, effortlessly integrating with PyTorch-based workflows.
For thorough step-by-step directions on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960
6.4 Inference with TRT-LLM (advised)
TensorRT-LLM now supports the DeepSeek-V3 model, providing accuracy choices such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in progress and will be launched quickly. You can access the custom-made branch of TRTLLM particularly for DeepSeek-V3 assistance through the following link to experience the brand-new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.
6.5 Inference with vLLM (recommended)
vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic methods, vLLM uses pipeline parallelism enabling you to run this model on numerous makers connected by networks. For comprehensive assistance, please describe the vLLM instructions. Please do not hesitate to follow the enhancement strategy as well.
6.6 Recommended Inference Functionality with AMD GPUs
In collaboration with the AMD group, we have actually accomplished Day-One support for AMD GPUs using SGLang, with complete compatibility for both FP8 and BF16 accuracy. For comprehensive assistance, please refer to the SGLang directions.
6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The MindIE structure from the Huawei Ascend neighborhood has actually successfully adjusted the BF16 variation of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the guidelines here.

7. License
This code repository is accredited under the MIT License. The usage of DeepSeek-V3 Base/Chat designs undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial use.