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Founded Date April 18, 1937
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Company Description
GitHub – Deepseek-ai/DeepSeek-V3
We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total criteria with 37B activated for each token. To accomplish efficient reasoning and economical training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger efficiency. We pre-train DeepSeek-V3 on 14.8 trillion varied and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to totally harness its abilities. Comprehensive evaluations expose that DeepSeek-V3 outperforms other open-source designs and attains performance equivalent to leading closed-source designs. Despite its excellent performance, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its full training. In addition, its training procedure is remarkably stable. Throughout the whole training process, we did not experience any irrecoverable loss spikes or carry out any rollbacks.
2. Model Summary
Architecture: Innovative Load Balancing Strategy and Training Objective
– On top of the effective architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free method for load balancing, which reduces the efficiency destruction that arises from encouraging load .
– We investigate a Multi-Token Prediction (MTP) objective and prove it helpful to model efficiency. It can likewise be used for speculative decoding for reasoning acceleration.
Pre-Training: Towards Ultimate Training Efficiency
– We design an FP8 combined accuracy training structure and, for the very first time, confirm the feasibility and effectiveness of FP8 training on a very large-scale model.
– Through co-design of algorithms, frameworks, and hardware, we get rid of the communication traffic jam in cross-node MoE training, nearly achieving full computation-communication overlap.
This significantly enhances our training efficiency and reduces the training costs, allowing us to further scale up the model size without extra 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 presently strongest open-source base design. The subsequent training stages after pre-training require just 0.1 M GPU hours.
Post-Training: Knowledge Distillation from DeepSeek-R1
– We introduce an innovative method to boil down reasoning abilities from the long-Chain-of-Thought (CoT) design, specifically from among the DeepSeek R1 series models, into basic LLMs, especially DeepSeek-V3. Our pipeline elegantly integrates the confirmation and reflection patterns of R1 into DeepSeek-V3 and significantly enhances its reasoning performance. Meanwhile, we also maintain a control over the output design and length of DeepSeek-V3.
3. Model Downloads
The total size of DeepSeek-V3 designs 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 optimal efficiency and versatility, we have actually partnered with open-source communities and hardware suppliers to offer several methods to run the design locally. For detailed assistance, check out Section 6: How_to Run_Locally.
For developers looking to dive much deeper, we suggest exploring README_WEIGHTS. md for information on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance 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 bold. Scores with a space not exceeding 0.3 are considered to be at the very same level. DeepSeek-V3 accomplishes the best efficiency on a lot of standards, particularly on mathematics and code tasks. For more examination 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 approximately 128K.
Chat Model
Standard Benchmarks (Models larger than 67B)
All designs are examined in a setup that restricts the output length to 8K. Benchmarks containing fewer than 1000 samples are evaluated several times utilizing differing temperature level settings to derive robust outcomes. DeepSeek-V3 stands as the best-performing open-source design, and likewise shows competitive performance against frontier closed-source models.
Open Ended Generation Evaluation
English open-ended discussion 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 offer OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com
6. How to Run Locally
DeepSeek-V3 can be released locally utilizing the following hardware and open-source community software application:
DeepSeek-Infer Demo: We offer a basic and light-weight demo for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables effective FP8 and BF16 inference for local and cloud deployment.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support 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 devices.
Since FP8 training is natively embraced in our framework, we just offer FP8 weights. If you need BF16 weights for experimentation, you can utilize the supplied conversion script to perform the improvement.
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 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 noted in requirements.txt. Easiest way is to utilize a plan manager like conda or uv to develop a new virtual environment and set up the dependencies.
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 particular format:
Run
Then you can chat with DeepSeek-V3:
Or batch reasoning on a provided file:
6.2 Inference with SGLang (advised)
SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing advanced latency and throughput efficiency among open-source frameworks.
Notably, SGLang v0.4.1 completely supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly versatile and robust option.
SGLang also supports multi-node tensor parallelism, enabling you to run this design on numerous network-connected makers.
Multi-Token Prediction (MTP) is in development, and development can be tracked in the optimization strategy.
Here are the launch instructions from the SGLang team: 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 structure customized for large language designs, now supports DeepSeek-V3. It offers both offline pipeline processing and online implementation capabilities, flawlessly incorporating with PyTorch-based workflows.
For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960
6.4 Inference with TRT-LLM (recommended)
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 development and will be released quickly. You can access the custom branch of TRTLLM particularly for DeepSeek-V3 support through the following link to experience the new features straight: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_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 standard techniques, vLLM offers pipeline parallelism permitting you to run this design on several makers linked by networks. For in-depth assistance, please refer to the vLLM directions. Please do not hesitate to follow the enhancement plan as well.
6.6 Recommended Inference Functionality with AMD GPUs
In cooperation with the AMD team, we have achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 accuracy. For in-depth assistance, please refer to the SGLang guidelines.
6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The MindIE structure from the Huawei Ascend neighborhood has actually successfully adapted the BF16 version of DeepSeek-V3. For step-by-step assistance on Ascend NPUs, please follow the guidelines here.
7. License
This code repository is licensed under the MIT License. Making use of DeepSeek-V3 Base/Chat designs goes through the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial usage.