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Founded Date Februar 12, 1977
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Company Description
GitHub – Deepseek-ai/DeepSeek-V3
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total specifications with 37B triggered for each token. To attain effective inference and economical training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free strategy for load balancing and sets a multi-token forecast training goal for more powerful efficiency. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to completely harness its capabilities. Comprehensive examinations reveal that DeepSeek-V3 outshines other open-source designs and accomplishes performance equivalent to leading closed-source designs. Despite its exceptional performance, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its full training. In addition, its training process is incredibly steady. Throughout the entire training process, 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 examine a Multi-Token Prediction (MTP) objective and show it beneficial to model efficiency. It can also be utilized for speculative decoding for inference velocity.
Pre-Training: Towards Ultimate Training Efficiency
– We design an FP8 blended accuracy training framework and, for the very first time, verify the feasibility and effectiveness of FP8 training on an exceptionally large-scale design.
– Through co-design of algorithms, frameworks, and hardware, we get rid of the communication bottleneck in cross-node MoE training, almost achieving full computation-communication overlap.
This significantly improves our training effectiveness and decreases the training costs, enabling us to further scale up the design size without additional overhead.
– At an economical expense of just 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the currently 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 innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, particularly from one of the DeepSeek R1 series designs, into basic LLMs, especially DeepSeek-V3. Our pipeline elegantly integrates the confirmation and reflection patterns of R1 into DeepSeek-V3 and notably enhances its reasoning performance. Meanwhile, we likewise preserve a control over the output design and length of DeepSeek-V3.
3. Model Downloads
The overall size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **
To ideal efficiency and flexibility, we have actually partnered with open-source communities and hardware suppliers to offer multiple ways to run the design in your area. For detailed assistance, take a look at Section 6: How_to Run_Locally.
For developers wanting to dive 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 assistance is presently under active advancement within the neighborhood, and we welcome your contributions and feedback.
4. Evaluation Results
Base Model
Standard Benchmarks
Best results are revealed in strong. Scores with a space not going beyond 0.3 are considered to be at the same level. DeepSeek-V3 attains the finest efficiency on most standards, particularly on math and code jobs. For more examination information, please check our paper.
Context Window
Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 carries out well across all context window lengths up to 128K.
Chat Model
Standard Benchmarks (Models bigger than 67B)
All models are evaluated in a configuration that restricts the output length to 8K. Benchmarks consisting of fewer than 1000 samples are checked multiple times using differing temperature settings to obtain robust outcomes. DeepSeek-V3 stands as the best-performing open-source design, and likewise displays competitive performance versus frontier closed-source models.
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 likewise provide OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com
6. How to Run Locally
DeepSeek-V3 can be deployed in your area using the following hardware and open-source neighborhood software:
DeepSeek-Infer Demo: We supply a simple and lightweight demo for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables efficient FP8 and BF16 inference for local and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 support coming soon.
vLLM: Support DeepSeek-V3 design 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 framework, we only supply FP8 weights. If you require BF16 weights for experimentation, you can utilize the provided conversion script to perform the transformation.
Here is an example of converting 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 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 reliances noted in requirements.txt. Easiest way is to utilize a package manager like conda or uv to produce a new virtual environment and set up the dependences.
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 specific format:
Run
Then you can chat with DeepSeek-V3:
Or batch reasoning on an offered file:
6.2 Inference with SGLang (suggested)
SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing state-of-the-art latency and throughput performance amongst open-source frameworks.
Notably, SGLang v0.4.1 totally supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it an extremely flexible and robust service.
SGLang also supports multi-node tensor parallelism, enabling you to run this design on several network-connected machines.
Multi-Token Prediction (MTP) is in advancement, and development can be tracked in the optimization strategy.
Here are the launch guidelines from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3
6.3 Inference with LMDeploy (recommended)
LMDeploy, a versatile and high-performance reasoning and serving structure customized for big language designs, now supports DeepSeek-V3. It provides both offline pipeline processing and online deployment abilities, seamlessly incorporating with PyTorch-based workflows.
For thorough detailed instructions on running DeepSeek-V3 with LMDeploy, please describe here: InternLM/lmdeploy # 2960
6.4 Inference with TRT-LLM (recommended)
TensorRT-LLM now supports the DeepSeek-V3 design, offering precision choices such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be launched quickly. You can access the customized branch of TRTLLM particularly for DeepSeek-V3 support 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 (suggested)
vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic methods, vLLM offers pipeline parallelism permitting you to run this model on multiple machines linked by networks. For detailed guidance, please refer to the vLLM directions. Please feel free to follow the improvement strategy as well.
6.6 Recommended Inference Functionality with AMD GPUs
In partnership with the AMD group, we have actually attained Day-One support for AMD GPUs utilizing SGLang, with complete compatibility for both FP8 and BF16 accuracy. For in-depth assistance, please describe the SGLang guidelines.
6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The MindIE framework from the Huawei Ascend community has effectively adapted the BF16 variation of DeepSeek-V3. For detailed guidance on Ascend NPUs, please follow the instructions here.
7. License
This code repository is accredited under the MIT License. The use of DeepSeek-V3 Base/Chat models goes through the Model License. DeepSeek-V3 series (including Base and Chat) supports business usage.