GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To attain efficient reasoning and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders 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 premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to totally harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outshines other open-source models and attains efficiency similar to leading closed-source designs. Despite its excellent efficiency, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its full training. In addition, its training procedure is extremely stable. Throughout the whole training procedure, 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 leader an auxiliary-loss-free method for load balancing, which lessens the efficiency destruction that occurs from encouraging load balancing.
– We examine a Multi-Token Prediction (MTP) goal and show it beneficial to design efficiency. It can also be used for speculative decoding for inference acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We design an FP8 combined precision training structure and, for the very first time, confirm the expediency and efficiency of FP8 training on an exceptionally large-scale design.
– Through co-design of algorithms, frameworks, and hardware, we get rid of the interaction traffic jam in cross-node MoE training, almost accomplishing full computation-communication overlap.
This substantially improves our training effectiveness and lowers the training costs, enabling us to even more scale up the design size without additional overhead.
– At an economical cost 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 phases after pre-training require just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an innovative method to distill reasoning abilities from the long-Chain-of-Thought (CoT) design, particularly from one of the DeepSeek R1 series models, into standard LLMs, especially DeepSeek-V3. Our pipeline elegantly integrates the verification and reflection patterns of R1 into DeepSeek-V3 and significantly improves its thinking efficiency. Meanwhile, we also preserve 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 ensure optimal performance and versatility, we have actually partnered with open-source communities and hardware suppliers to supply numerous ways to run the model in your area. For step-by-step assistance, check out Section 6: How_to Run_Locally.

For developers looking to dive much deeper, we suggest 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 welcome your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are shown in strong. Scores with a space not going beyond 0.3 are considered to be at the very same level. DeepSeek-V3 achieves the very best performance on many criteria, specifically on mathematics and code jobs. For more examination details, please check our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All designs are examined in a configuration that restricts the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature level settings to derive robust results. DeepSeek-V3 stands as the best-performing open-source model, and also shows competitive performance versus frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended discussion evaluations. For AlpacaEval 2.0, we utilize 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 locally utilizing the following hardware and open-source community software:

DeepSeek-Infer Demo: We provide a simple 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 soon.
LMDeploy: Enables effective FP8 and BF16 reasoning for local and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 assistance 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 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 only offer FP8 weights. If you need BF16 weights for experimentation, you can use the supplied conversion script to carry out 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 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 install 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 reliances.

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

Model Weights Conversion

Convert Hugging Face design weights to a particular format:

Run

Then you can chat with DeepSeek-V3:

Or batch reasoning on a given file:

6.2 Inference with SGLang (suggested)

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

Notably, SGLang v0.4.1 fully supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it an extremely versatile and robust solution.

SGLang likewise supports multi-node tensor parallelism, allowing you to run this model on several network-connected machines.

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

Here are the launch directions from the SGLang group: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3

6.3 Inference with LMDeploy (recommended)

LMDeploy, a flexible and high-performance inference and serving framework customized for large language models, now supports DeepSeek-V3. It uses both offline pipeline processing and online implementation abilities, flawlessly integrating with PyTorch-based workflows.

For comprehensive step-by-step guidelines 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 design, providing accuracy choices such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be launched soon. You can access the custom-made 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 (advised)

vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic techniques, vLLM uses pipeline parallelism permitting you to run this model on several makers connected by networks. For detailed guidance, please refer to the vLLM directions. Please feel free to follow the enhancement plan too.

6.6 Recommended Inference Functionality with AMD GPUs

In cooperation with the AMD group, we have actually accomplished Day-One assistance for AMD GPUs utilizing SGLang, with complete compatibility for both FP8 and BF16 precision. For detailed assistance, please refer to the SGLang guidelines.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

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

7. License

This code repository is licensed under the MIT License. Using DeepSeek-V3 Base/Chat designs is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial usage.